{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os, cv2, dlib, re, pyrender, trimesh\n",
    "import numpy as np\n",
    "import scipy.io as sio\n",
    "import os.path as osp\n",
    "from scipy.io import loadmat\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.spatial.transform import Rotation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def loadObj(file):\n",
    "    obj = {}\n",
    "    obj['v'],obj['vn'],obj['vt'],obj['f'] = [],[],[],[]\n",
    "    f = open(file, \"r\")\n",
    "    lines = f.readlines()\n",
    "    delimiters = \" \", \"//\", \"\\\\\",\"/\",\"\\\\\"\n",
    "    regexPattern = '|'.join(map(re.escape, delimiters))\n",
    "    for item in lines:\n",
    "        data = re.split(regexPattern, item)\n",
    "        if 'v' == data[0]:\n",
    "            obj['v'].append(np.array(data[1:]).astype('float'))\n",
    "        elif 'vt' == data[0]:\n",
    "            obj['vt'].append(np.array(data[1:]).astype('float'))\n",
    "        elif 'vn' == data[0]:\n",
    "            obj['vn'].append(np.array(data[1:]).astype('float'))\n",
    "        elif 'f' == data[0]:\n",
    "            obj['f'].append(np.array(data[1:]).astype('int32'))\n",
    "    \n",
    "    hasVN,hasVT = False,False\n",
    "    if len(obj['v']):\n",
    "        obj['v'] = np.vstack(obj['v'])\n",
    "    if len(obj['vn']):\n",
    "        hasVN = True\n",
    "        obj['vn'] = np.vstack(obj['vn'])\n",
    "    if len(obj['vt']):\n",
    "        hasVT = True\n",
    "        obj['vt'] = np.vstack(obj['vt'])\n",
    "    if len(obj['f']):\n",
    "        obj['f'] = np.vstack(obj['f'])\n",
    "    \n",
    "    f = np.ones((9,obj['f'].shape[0]))\n",
    "    if  hasVN and hasVT:\n",
    "        f = obj['f'][:,[0,3,6,2,5,8,1,4,7]].T\n",
    "    elif hasVN and obj['f'].shape[1]==6:\n",
    "        f[:6] = obj['f'][:,[0,2,4,1,3,5]].T\n",
    "    elif hasVT and obj['f'].shape[1]==6:\n",
    "        f[[0,1,2,6,7,8]] = obj['f'][:,[0,2,4,1,3,5]].T\n",
    "    else:\n",
    "        f[[0,1,2]] = obj['f'].T\n",
    "    obj['f'] = f.astype('uint32')-1\n",
    "    return obj\n",
    "\n",
    "def saveObj(file,obj):\n",
    "    f = open(file, \"w\")\n",
    "    for item in obj['v']:\n",
    "        print('v %.4f %.4f %.4f'%tuple(item),file=f)\n",
    "    if len(obj['vn']):\n",
    "        for item in obj['vn']:\n",
    "            print('vn %.4f %.4f %.4f'%tuple(item),file=f)\n",
    "    if len(obj['vt']):\n",
    "        for item in obj['vt']:\n",
    "            print('vt %.4f %.4f'%tuple(item),file=f)\n",
    "       \n",
    "    for item in obj['f'].T+1:\n",
    "        if len(obj['vn']) and len(obj['vt']):\n",
    "            print('f %d/%d/%d %d/%d/%d %d/%d/%d'%tuple(item[[0,3,6,2,5,8,1,4,7]]),file=f)\n",
    "        elif len(obj['vn']) :\n",
    "            print('f %d/%d %d/%d %d/%d'%tuple(item[[0,3,1,4,2,5]]),file=f)\n",
    "        elif len(obj['vt']):\n",
    "            print('f %d/%d %d/%d %d/%d'%tuple(item[[0,6,1,7,2,8]]),file=f)\n",
    "        else:\n",
    "            print('f %d %d %d'%tuple(item[:3]),file=f)\n",
    "    f.close()\n",
    "\n",
    "def rect_to_bb_padding(rect,h,w,x_para = 1.0 / 2,y_para = 1.0 / 2):\n",
    "    x_len = rect.right() - rect.left()\n",
    "    y_len = rect.bottom() - rect.top()\n",
    "    left = max(  int(rect.left() - x_para * x_len), 0)\n",
    "    right = min(  int(rect.right() + x_para * x_len-1), w - 1)\n",
    "    top = max( int(rect.top() - 1.4*y_para * y_len) ,0)\n",
    "    bottom = min( int(rect.bottom() + 0.6*y_para * y_len-1), h - 1)\n",
    "    return np.array([left, top, right-left, bottom-top])\n",
    "\n",
    "def rect_to_bb(rect):\n",
    "    # take a bounding predicted by dlib and convert it\n",
    "    # to the format (x, y, w, h) as we would normally do\n",
    "    # with OpenCV\n",
    "    x = rect.left()\n",
    "    y = rect.top()\n",
    "    w = rect.right() - x\n",
    "    h = rect.bottom() - y\n",
    "\n",
    "    # return a tuple of (x, y, w, h)\n",
    "    return np.array([x, y, w, h])\n",
    "\n",
    "def landmark_detect(gray):\n",
    "    rects = detector(gray, 1)\n",
    "    if len(rects)==0:\n",
    "        return None,None\n",
    "   \n",
    "    h,w = gray.shape[:2]\n",
    "    bbox = rect_to_bb_padding(rects[0],h,w)\n",
    "    gray = gray[bbox[1]:bbox[1]+bbox[3],bbox[0]:bbox[0]+bbox[2]]\n",
    "    rects = detector(gray, 1)\n",
    "    if len(rects)==0:\n",
    "        return None,None\n",
    "    \n",
    "    rect = rects[0]\n",
    "    shape = predictor(gray, rect)\n",
    "    lm = np.array([[p.x+bbox[0], p.y+bbox[1]] for p in shape.parts()])\n",
    "\n",
    "    return lm, bbox\n",
    "\n",
    "def angle2matrix(pose):\n",
    "    ''' compute Rotation Matrix from three Euler angles\n",
    "    '''\n",
    "    R_x = np.array([[1, 0, 0],\n",
    "                    [0, np.cos(pose[0]), -np.sin(pose[0])],\n",
    "                    [0, np.sin(pose[0]), np.cos(pose[0])]\n",
    "                    ])\n",
    "\n",
    "    R_y = np.array([[np.cos(pose[1]), 0, np.sin(pose[1])],\n",
    "                    [0, 1, 0],\n",
    "                    [-np.sin(pose[1]), 0, np.cos(pose[1])]\n",
    "                    ])\n",
    "\n",
    "    R_z = np.array([[np.cos(pose[2]), -np.sin(pose[2]), 0],\n",
    "                    [np.sin(pose[2]), np.cos(pose[2]), 0],\n",
    "                    [0, 0, 1]\n",
    "                    ])\n",
    "    R = np.dot(R_z, np.dot(R_y, R_x))\n",
    "    return R\n",
    "\n",
    "def isRotationMatrix(R):\n",
    "    Rt = np.transpose(R)\n",
    "    shouldBeIdentity = np.dot(Rt, R)\n",
    "    I = np.identity(3, dtype=R.dtype)\n",
    "    n = np.linalg.norm(I - shouldBeIdentity)\n",
    "    return n < 1e-6\n",
    "\n",
    "def matrix2angle(R):\n",
    "    #assert (isRotationMatrix(R))\n",
    "\n",
    "    sy = np.sqrt(R[0, 0] * R[0, 0] + R[1, 0] * R[1, 0])\n",
    "\n",
    "    singular = sy < 1e-6\n",
    "\n",
    "    if not singular:\n",
    "        x = np.arctan2(R[2, 1], R[2, 2])\n",
    "        y = np.arctan2(-R[2, 0], sy)\n",
    "        z = np.arctan2(R[1, 0], R[0, 0])\n",
    "    else:\n",
    "        x = np.arctan2(-R[1, 2], R[1, 1])\n",
    "        y = np.arctan2(-R[2, 0], sy)\n",
    "        z = 0\n",
    "\n",
    "    return np.array([x, y, z])\n",
    "\n",
    "\n",
    "def save_to_pts(lm,file):\n",
    "    f = open(file,'w')\n",
    "    print('version: 1\\nn_points:  68\\n{',file=f)\n",
    "    for i in range(68):\n",
    "        print('%d %d'%(lm[i,0],lm[i,1]),file=f)\n",
    "    print('}',file=f)\n",
    "    f.close()\n",
    "    \n",
    "def shape_to_np(shape, dtype=\"int\"):\n",
    "    coords = np.zeros((68, 2), dtype=dtype)\n",
    "\n",
    "    for i in range(0, 68):\n",
    "        coords[i] = (np.round(shape.part(i).x), np.round(shape.part(i).y))\n",
    "\n",
    "    return coords\n",
    "\n",
    "def load_imgs(path,size=512):\n",
    "    imgs,pts = [],[]\n",
    "    if os.path.isdir(path):\n",
    "        files = os.listdir(path)\n",
    "    else:\n",
    "        files = [path]\n",
    "    for item in files:\n",
    "        file = os.path.join(path,item)\n",
    "        img = cv2.imread(file)\n",
    "        downScele = np.min(img.shape[:2])/512\n",
    "        downShape = np.round(img.shape[:2]/downScele).astype('int')\n",
    "        gray = cv2.resize(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY),(downShape[1],downShape[0]))\n",
    "        lm,_ = landmark_detect(gray)\n",
    "        if lm is None:\n",
    "            continue\n",
    "\n",
    "        downScale = img.shape[:2]/downShape\n",
    "        pts.append(lm*downScale)\n",
    "        imgs.append(img)\n",
    "    pts,imgs = np.stack(pts,axis=0),np.stack(imgs,axis=0)\n",
    "    return imgs, pts\n",
    "\n",
    "def rescale_bbox(newShape,oldShape,bbox,lm):\n",
    "    downScale = newShape/oldShape\n",
    "    lm = lm*downScale[[1,0]]\n",
    "    bbox[[0,2]] = (bbox[[0,2]]*downScale[1]).astype('int')\n",
    "    bbox[[1,3]] = (bbox[[1,3]]*downScale[0]).astype('int')\n",
    "    return bbox,lm\n",
    "    \n",
    "def load_imgs_and_centercrop(path,size=512):\n",
    "    imgs,pts = [],[]\n",
    "    if os.path.isdir(path):\n",
    "        files = os.listdir(path)\n",
    "    else:\n",
    "        files = [path]\n",
    "    for item in files:\n",
    "        file = os.path.join(path,item)\n",
    "        img = cv2.imread(file)\n",
    "        downScele = np.min(img.shape[:2])/512\n",
    "        downShape = np.round(img.shape[:2]/downScele).astype('int')\n",
    "        gray = cv2.resize(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY),(downShape[1],downShape[0]))\n",
    "        lm, bbox = landmark_detect(gray)\n",
    "        if lm is None:\n",
    "            continue\n",
    "        \n",
    "        bbox,lm = rescale_bbox(img.shape[:2], downShape,bbox,lm)\n",
    "\n",
    "        # shift to [0,0]\n",
    "        img = img[bbox[1]:bbox[1]+bbox[3],bbox[0]:bbox[0]+bbox[2]]\n",
    "        lm -= bbox[:2].reshape((1,2))\n",
    "        \n",
    "        img = cv2.resize(img,(size,size))\n",
    "        bbox,lm = rescale_bbox(np.array([size,size]),bbox[[3,2]],bbox,lm)\n",
    "        \n",
    "        pts.append(lm)\n",
    "        imgs.append(img)\n",
    "    pts,imgs = np.stack(pts,axis=0),np.stack(imgs,axis=0)\n",
    "    return imgs, pts\n",
    "\n",
    "def ortho(frustum):\n",
    "    left,right,bottom,top = frustum[:]\n",
    "    projection = np.eye(4)\n",
    "    projection[0,0], projection[1,1],projection[2,2] = 2/(right - left), 2/(top - bottom), -1\n",
    "    projection[0,3],projection[1,3] = -(right + left) / (right - left),  -(top + bottom) / (top - bottom)\n",
    "    return projection\n",
    "\n",
    "def render(mesh,fy,cam=np.eye(4),model=np.eye(4),resolution=[512,512]):\n",
    "\n",
    "    obj = pyrender.Mesh.from_trimesh(mesh, smooth=True)\n",
    "    fovY = 2*np.arctan(resolution[0]/2/fy)\n",
    "    # compose scene\n",
    "    scene = pyrender.Scene(ambient_light=[0.1, 0.1, 0.1], bg_color=[0, 0, 0])\n",
    "    camera = pyrender.PerspectiveCamera( yfov=fovY)\n",
    "    light = pyrender.DirectionalLight(color=[1.0, 1.0, 1.0], intensity=8)\n",
    "\n",
    "    scene.add(obj, pose=  model)\n",
    "    scene.add(light, pose=  model)\n",
    "    scene.add(camera, pose = cam)\n",
    "    \n",
    "    # render scene\n",
    "    r = pyrender.OffscreenRenderer(resolution[1], resolution[0])\n",
    "    color, depth = r.render(scene)\n",
    "    color,depth = color[::-1,::-1],depth[::-1,::-1]\n",
    "    return color,depth\n",
    "\n",
    "def render2(mesh,cam=np.eye(4),model=np.eye(4),scale=[1,1],resolution=[720,1280]):\n",
    "\n",
    "    obj = pyrender.Mesh.from_trimesh(mesh, smooth=True)\n",
    "\n",
    "    scene = pyrender.Scene(ambient_light=[1.0, 1.0, 1.0], bg_color=[0, 0, 0])\n",
    "    camera = pyrender.OrthographicCamera(xmag=1, ymag=resolution[0]/2/scale[1], znear=1e-3, zfar=10000.0)\n",
    "    light = pyrender.DirectionalLight(color=[1.0, 1.0, 1.0], intensity=2.0)\n",
    "\n",
    "    scene.add(obj, pose=  model)\n",
    "    scene.add(light, pose=  model)\n",
    "    scene.add(camera, pose = cam)\n",
    "    \n",
    "    # render scene\n",
    "    r = pyrender.OffscreenRenderer(resolution[1], resolution[0])\n",
    "    color, _ = r.render(scene)\n",
    "    \n",
    "    width = int(round(resolution[1]*scale[0]/scale[1]))\n",
    "    img = cv2.resize(color,(width,resolution[0]))\n",
    "    pad = (width - resolution[1])//2\n",
    "    right_pad = resolution[1] - width - abs(pad)\n",
    "    if pad >= 0:\n",
    "        img = img[:,pad:pad+resolution[1]]\n",
    "    elif pad < 0:\n",
    "        img = np.pad(img, ((0,0),(abs(pad),right_pad),(0,0)), 'edge')\n",
    "    \n",
    "    return img,_\n",
    "\n",
    "def solveCamera(model_points,image_points):\n",
    "    if 'ortho' == CameraMode:\n",
    "        extrinsic, affine, cameraMatrix = solveCamera_ortho(model_points,image_points)\n",
    "    else:\n",
    "        extrinsic, affine, cameraMatrix = solveCamera_perspect(model_points,image_points)\n",
    "    return extrinsic, affine, cameraMatrix\n",
    "        \n",
    "def solveCamera_perspect(model_points,image_points):\n",
    "    W,H = img_size\n",
    "    fov_y = 15\n",
    "    fov_y = fov_y/180*np.pi\n",
    "    f = H/2/np.tan(fov_y/2)\n",
    "\n",
    "    cx,cy = W/2,H/2\n",
    "    cameraMatrix = np.array([[f,0,cx],[0,f,cy],[0,0,1]]).astype('float')\n",
    "    distCoeffs = np.zeros((1,4))\n",
    "    \n",
    "    _, rvec, t = cv2.solvePnP(model_points, image_points,cameraMatrix,distCoeffs)#solvePnPRansac\n",
    "    r = Rotation.from_rotvec(rvec.reshape((-1))).as_matrix()\n",
    "    \n",
    "    extrins = np.hstack([r,t])\n",
    "    extrins = np.vstack([extrins,np.array([0,0,0,1])])\n",
    "    \n",
    "#     vertice = np.hstack((model_points,np.ones((model_points.shape[0],1))))\n",
    "#     vertice = np.dot(cameraMatrix,np.dot(extrins,vertice.T)[:3])\n",
    "#     vertice /= vertice[[2]]\n",
    "#     print(vertice[:2].T,image_points)\n",
    "\n",
    "    affine = np.dot(cameraMatrix,extrins[:3])[:3]\n",
    "    return extrins, affine,cameraMatrix\n",
    "\n",
    "def solveCamera_ortho(model_points,image_points):\n",
    "    # model_points, image_points N*M\n",
    "    A,b = np.zeros((2*model_points.shape[0],8)),np.zeros((2*model_points.shape[0],1))\n",
    "    ind = 0\n",
    "    for model_point, image_point in zip(model_points,image_points):\n",
    "        A[2*ind,:3],A[2*ind+1,4:7] = model_point[:3],model_point[:3]\n",
    "        A[2*ind,3],A[2*ind+1,7] = 1,1\n",
    "        b[2*ind:2*ind+2] = image_point.reshape((-1,1))\n",
    "        ind += 1\n",
    "    affine,_,_,_ = np.linalg.lstsq(A,b,rcond=None)\n",
    "    affine = affine.reshape((2,4))\n",
    "    \n",
    "    \n",
    "    norms = np.linalg.norm(affine[:,:3],axis=1)\n",
    "    scale = norms\n",
    "    R = affine[:,:3]/norms.reshape((-1,1))\n",
    "    R1,R2 = R[0],R[1]\n",
    "    R3 = np.cross(R1,R2)\n",
    "    R3 /= np.linalg.norm(R3)\n",
    "    R = np.vstack((R1,R2,R3))\n",
    "\n",
    "    U,S,V = np.linalg.svd(R)\n",
    "    R_ortho = np.dot(U, V)\n",
    "    if np.linalg.det(R_ortho) < 0:\n",
    "        U[2,0] = -U[2,0]\n",
    "        R_ortho = np.dot(U, V)\n",
    "    t1,t2 = affine[0,3],affine[1,3]\n",
    "\n",
    "    MV = np.zeros((4,4))\n",
    "    MV[:3,:3] = R_ortho[:3]\n",
    "    MV[:2,3]  = (affine[:2,3] - np.array(img_size)/2)/scale\n",
    "    MV[3,3],MV[2,3] = 1, -1\n",
    "    \n",
    "#     t1,t2 = affine[0,3]/scale[0],affine[1,3]/scale[1]\n",
    "#     view_model = np.eye(4)\n",
    "#     view_model[:3,:3] = R_ortho[:3]\n",
    "#     view_model[:2,3]  = np.array([t1,t2])\n",
    "#     frustum = np.array([0.0,img_size[0]/scale[0],0.0,img_size[1]/scale[1]])\n",
    "#     ortho_projection = ortho(frustum)\n",
    "#     mvp = np.dot(ortho_projection, view_model)\n",
    "#     mvp[2,3] = -2\n",
    "#     viewport = np.array([0, img_size[1], img_size[0], -img_size[1]]) # flips y, origin top-left, like in OpenCV\n",
    "#     viewport_mat = np.array( [[viewport[2] / 2.0, 0.0, 0.0, viewport[2] / 2.0 + viewport[0]], \\\n",
    "#                               [0.0,               viewport[3] / 2.0, 0.0, viewport[3] / 2.0 + viewport[1]], \\\n",
    "#                               [0.0,               0.0,               1.0, 0.0], \\\n",
    "#                               [0.0,               0.0,               0.0, 1.0]]) \n",
    "#     full_projection = np.dot(viewport_mat, mvp)\n",
    "    affine = np.vstack((affine,np.array([0,0,0,1])))\n",
    "    return MV, affine, scale\n",
    "\n",
    "def findNearestCooresponding(image_points,model_points,affine):\n",
    "    points = np.hstack((model_points,np.ones((model_points.shape[0],1))))\n",
    "    reProjection = np.dot(affine,points.T).T\n",
    "    if reProjection.shape[1]>2:\n",
    "        reProjection /= reProjection[:,[2]]\n",
    "    model_ids = []\n",
    "    for item in image_points:\n",
    "        dis = np.sum(np.abs(reProjection[:,:2] - item.reshape((1,-1))),axis=1)\n",
    "        model_ids.append(np.argmin(dis))\n",
    "    return model_points[model_ids],model_ids\n",
    "\n",
    "def visLM(model_points, image_points, im,affine, homogeneous=False):\n",
    "    if homogeneous:\n",
    "        points = np.hstack((model_points,np.ones((model_points.shape[0],1))))\n",
    "    else:\n",
    "        points = model_points\n",
    "    points = np.dot(affine,points.T).T\n",
    "    points /= points[:,[2]]\n",
    "    for p in points:\n",
    "        cv2.circle(im, (int(p[0]), int(im.shape[0] - p[1])), 1, (0,255,0), -1)  \n",
    "    for p in image_points:\n",
    "        cv2.circle(im, (int(p[0]), int(im.shape[0] - p[1])), 1, (0,0,255), -1)  \n",
    "    return im\n",
    "\n",
    "def calReprojecErr(affine,point,lm):\n",
    "    point_img = np.dot(affine[:2,:3],point.T)+affine[:2,[3]]\n",
    "    return np.mean(np.abs(lm-point_img.T))                 \n",
    "    \n",
    "def buildEdgeTopology(mesh):\n",
    "    edges = []\n",
    "    for item in mesh.faces:\n",
    "        edges.append([item[0],item[1]])\n",
    "        edges.append([item[1],item[2]])\n",
    "        edges.append([item[2],item[0]])\n",
    "    return np.vstack(edges)\n",
    "\n",
    "def isFront(mesh,point_id):\n",
    "    z = mesh.vertices[:,2][point_id]\n",
    "    mask = z[:,0]>z[:,1]\n",
    "    front_id = np.unique(np.hstack((point_id[mask,0],point_id[~mask,1])))\n",
    "    return front_id\n",
    "\n",
    "def pointCloudClustering(vertices,thread):\n",
    "    isTravel = np.zeros(vertices.shape[0]).astype('bool')\n",
    "    while np.sum(isTravel) < vertices.shape[0]:\n",
    "        sample = np.where(isTravel)[0]\n",
    "        point = vertices[sample]\n",
    "        dis = np.sum(np.abs(vertices - point.reshape((1,-1))),axis=0)/3\n",
    "        ind = np.where(dis<thread)\n",
    "#         if isTravel[ind]\n",
    "    \n",
    "def findOcclusionEdge(mesh,MV,affine, contourLM, edge):\n",
    "    normal = np.dot(MV[:3,:3],mesh.vertex_normals.T).T\n",
    "    isBoundaries = normal[:,2][edge]>0\n",
    "    isBoundaries = isBoundaries[:,0]!=isBoundaries[:,1]\n",
    "    isBoundaries = np.where(isBoundaries)\n",
    "    point_id = []\n",
    "    for item in isBoundaries:\n",
    "        point_id.append(edge[item])\n",
    "    point_id = np.hstack(point_id)\n",
    "    \n",
    "    # Compute vertices that lye on occluding boundaries:\n",
    "    point_id = isFront(mesh,point_id)\n",
    "    points = mesh.vertices[point_id]\n",
    "    \n",
    "    # Project 3D boundaries to 2D\n",
    "    points = np.hstack((points,np.ones((points.shape[0],1))))\n",
    "    points_2D = np.dot(affine,points.T).T\n",
    "    \n",
    "    # Filter points far away\n",
    "    ids,ids_img = [],[]\n",
    "    threadHood = np.sum(np.abs(contourLM[0]-contourLM[-1]))/25\n",
    "    for i,item in enumerate(points_2D):\n",
    "        if  not contourMask[point_id[i]]:\n",
    "            continue\n",
    "\n",
    "        dis = np.sum(np.abs(contourLM-item.reshape((1,-1))),axis=1)\n",
    "        minimal_id = np.argmin(dis)\n",
    "        if dis[minimal_id] < threadHood:\n",
    "            ids.append(i)\n",
    "            ids_img.append(minimal_id)\n",
    "    point_id = point_id[ids]\n",
    "    return mesh.vertices[point_id],contourLM[ids_img], point_id\n",
    "\n",
    "def solvePCA(obj_base,affine,w,lm_base,ids,MODE,num_coeffs_to_fit=10,lamb=10.0):\n",
    "    #  affine*(v_base+ w_base*coef) = lm\n",
    "    # v_base n*3, w_base n*useBasis, coef useBasis*1, affine 3*4\n",
    "\n",
    "    if affine.ndim<3:\n",
    "        affine,lm_base = affine[np.newaxis],lm_base[np.newaxis]\n",
    "    \n",
    "    index,steps = [],[0]\n",
    "    for i in range(len(affine)):\n",
    "        steps.extend([len(lm_base[i])+steps[i]])\n",
    "        index.extend(list(np.ones(1).astype('uint8').repeat(len(lm_base[i]))*i))\n",
    "    \n",
    "\n",
    "    v_ind = ids['model_%s_ids'%MODE]\n",
    "    v_base = obj_base.reshape((-1,3))[v_ind]#\n",
    "    w_base = w.reshape((-1,3,w.shape[-1]))[v_ind][...,:num_coeffs_to_fit].reshape((-1,num_coeffs_to_fit))\n",
    "    \n",
    "    num_landmarks = v_base.shape[0]\n",
    "    y,v_bar = np.ones(3*num_landmarks),np.ones(4*num_landmarks)\n",
    "    V_hat_h = np.zeros((4 * num_landmarks, num_coeffs_to_fit))\n",
    "    P = np.zeros((3 * num_landmarks, 4 * num_landmarks))\n",
    "    for i in range(num_landmarks):\n",
    "        V_hat_h[i*4:i*4+3] = w_base[i*3:i*3+3]\n",
    "        P[i*3:i*3+3,i*4:i*4+4] = affine[index[i]]\n",
    "        y[i*3:i*3+2] = lm_base[index[i]][i-steps[index[i]]]\n",
    "        v_bar[4*i:4*i+3] = v_base[i]\n",
    "        \n",
    "    sigma_squared_2D = 3\n",
    "    Omega = np.eye(3 * num_landmarks)/sigma_squared_2D\n",
    "    \n",
    "    A = np.dot(P, V_hat_h)\n",
    "    b = np.dot(P, v_bar) - y\n",
    "    AtOmegaAReg = np.dot(np.dot(A.T,Omega), A) + lamb * np.eye(num_coeffs_to_fit)\n",
    "    rhs = - np.dot(np.dot(A.T, Omega), b)\n",
    "    \n",
    "    \n",
    "    # solve with qr\n",
    "    Q, R = np.linalg.qr(AtOmegaAReg)\n",
    "    y = np.dot(Q.T, rhs)\n",
    "    param = np.linalg.solve(R, y).reshape((-1,1))\n",
    "    obj_with_exp = obj_base + np.dot(w[...,:num_coeffs_to_fit],param)\n",
    "#     print(calReprojecErr(affine,obj_with_exp.reshape((-1,3))[ids['model_exp_ids']],lm_base))\n",
    "    return obj_with_exp,param\n",
    "\n",
    "    \n",
    "def optimizePose(mesh,lm,ids,steps=10,sha_coeffs_to_fit=50, exp_coeffs_to_fit=10,is_optimize_shape=True): \n",
    "    \n",
    "    image_5point_ids,model_5point_ids,model_ids,image_ids = ids['image_5point_ids'],ids['model_5point_ids'],ids['model_ids'],ids['image_ids']\n",
    "    model_points = mesh.vertices[model_5point_ids].astype('float32')\n",
    "    image_points = lm[image_5point_ids].astype('float32')\n",
    "    MV,affine,scale = solveCamera(model_points,image_points)\n",
    "\n",
    "    shape_coef, exp_coef = np.zeros((sha_coeffs_to_fit,1)), np.zeros((exp_coeffs_to_fit,1))\n",
    "    for iter in range(steps):\n",
    "\n",
    "        threadHood = 7.5/180*np.pi\n",
    "        angle = matrix2angle(MV[:3,:3])\n",
    "        model_points = mesh.vertices[model_5point_ids].astype('float32')\n",
    "        image_points = lm[image_5point_ids].astype('float32')\n",
    "        model_ids_new = np.array(model_5point_ids)\n",
    "\n",
    "        if  angle[1] < threadHood:\n",
    "            image_points_add, model_points_add = lm[image_ids[1]],mesh.vertices[model_ids[1]]\n",
    "            model_points_add, model_ids_add = findNearestCooresponding(image_points_add, model_points_add,affine)\n",
    "            \n",
    "            image_points = np.vstack((image_points,image_points_add))\n",
    "            model_points = np.vstack((model_points,model_points_add))\n",
    "            model_ids_new = np.hstack((model_ids_new,model_ids[1][model_ids_add]))\n",
    "\n",
    "        if angle[1] > -threadHood:\n",
    "            image_points_add, model_points_add = lm[image_ids[0]],mesh.vertices[model_ids[0]]\n",
    "            model_points_add, model_ids_add = findNearestCooresponding(image_points_add, model_points_add,affine)\n",
    "\n",
    "            image_points = np.vstack((image_points,image_points_add))\n",
    "            model_points = np.vstack((model_points,model_points_add))\n",
    "            model_ids_new = np.hstack((model_ids_new,model_ids[0][model_ids_add]))\n",
    "\n",
    "        MV,affine,scale = solveCamera(model_points,image_points)\n",
    "        contour_3D,contour_2D,model_ids_add = fitOccludingContour(mesh,angle,MV,affine[:2],lm,image_ids,edge)\n",
    "        image_points_temp = np.vstack((image_points,contour_2D))\n",
    "        model_points_temp = np.vstack((model_points,contour_3D))\n",
    "       \n",
    "        MV,affine,scale = solveCamera(model_points_temp,image_points_temp)\n",
    "        \n",
    "        angle = matrix2angle(MV[:3,:3])\n",
    "        contour_3D,contour_2D,model_ids_add = fitOccludingContour(mesh,angle,MV,affine[:2],lm,image_ids,edge)\n",
    "        image_points = np.vstack((image_points,contour_2D))\n",
    "        model_points = np.vstack((model_points,contour_3D))\n",
    "        model_ids_new = np.hstack((model_ids_new,model_ids_add))\n",
    "\n",
    "        if not is_optimize_shape:\n",
    "            return MV,affine,scale,image_points,model_ids_new,mesh\n",
    "        \n",
    "#         affine2 = np.vstack((affine,np.array([0,0,0,1])))\n",
    "        # optimize shape\n",
    "        ids['model_sha_ids'] = model_ids_new\n",
    "        obj_current = obj_base + np.dot(models['w_exp'][...,:exp_coeffs_to_fit],exp_coef)\n",
    "        _, shape_coef = solvePCA(obj_current,affine,models['w_shp'],image_points,ids,MODE='sha',num_coeffs_to_fit=sha_coeffs_to_fit)\n",
    "\n",
    "        # optimize expression\n",
    "        obj_current = obj_base + np.dot(models['w_shp'][...,:sha_coeffs_to_fit],shape_coef)\n",
    "        vertice,exp_coef = solvePCA(obj_current,affine,models['w_exp'],lm[ids['image_exp_ids']],ids,MODE='exp',num_coeffs_to_fit=exp_coeffs_to_fit)\n",
    "    \n",
    "        mesh.vertices = vertice.reshape((-1,3))\n",
    "    \n",
    "    return MV,affine,scale,image_points,model_ids_new,mesh\n",
    "\n",
    "def optimizePose_Multiply_Img(mesh,lm,ids,steps=10,sha_coeffs_to_fit=40, exp_coeffs_to_fit=10): \n",
    "    # lm: [N,68,2]\n",
    "    image_5point_ids,model_5point_ids,model_ids,image_ids = ids['image_5point_ids'],ids['model_5point_ids'],ids['model_ids'],ids['image_ids']\n",
    "    model_points = mesh.vertices[model_5point_ids].astype('float32')\n",
    "    image_points = lm[:,image_5point_ids].astype('float32')\n",
    "    \n",
    "    MVs,affines,scales = [],[],[]\n",
    "    for i in range(len(image_points)):\n",
    "        MV,affine,scale = solveCamera(model_points,image_points[i])\n",
    "        MVs.append(MV);affines.append(affine);scales.append(scale)\n",
    "\n",
    "    threadHood = 5.5/180*np.pi\n",
    "    shape_coef, exp_coef = np.zeros((sha_coeffs_to_fit,1)), np.zeros((exp_coeffs_to_fit,1))\n",
    "    for iter in range(steps):\n",
    "     \n",
    "        model_ids_new2, image_points2 = [],[]\n",
    "        affines2,MVs2, scales2 = [],[],[]\n",
    "        for i,(MV,affine) in enumerate(zip(MVs,affines)):\n",
    "            angle = matrix2angle(MV[:3,:3])\n",
    "\n",
    "            model_points = mesh.vertices[model_5point_ids].astype('float32')\n",
    "            image_points = lm[i,image_5point_ids].astype('float32')\n",
    "            model_ids_new = np.array(model_5point_ids)\n",
    "\n",
    "            if  angle[1] < threadHood and angle[1] > -2*threadHood:\n",
    "                image_points_add, model_points_add = lm[i,image_ids[1]],mesh.vertices[model_ids[1]]\n",
    "                model_points_add, model_ids_add = findNearestCooresponding(image_points_add, model_points_add,affine)\n",
    "\n",
    "                image_points = np.vstack((image_points,image_points_add))\n",
    "                model_points = np.vstack((model_points,model_points_add))\n",
    "                model_ids_new = np.hstack((model_ids_new,model_ids[1][model_ids_add]))\n",
    "\n",
    "            if angle[1] > -threadHood and angle[1] < 2*threadHood:\n",
    "                image_points_add, model_points_add = lm[i,image_ids[0]],mesh.vertices[model_ids[0]]\n",
    "                model_points_add, model_ids_add = findNearestCooresponding(image_points_add, model_points_add,affine)\n",
    "\n",
    "                image_points = np.vstack((image_points,image_points_add))\n",
    "                model_points = np.vstack((model_points,model_points_add))\n",
    "                model_ids_new = np.hstack((model_ids_new,model_ids[0][model_ids_add]))\n",
    "\n",
    "            MV,affine,scale = solveCamera(model_points,image_points)\n",
    "            contour_3D,contour_2D,model_ids_add = fitOccludingContour(mesh,angle,MV,affine[:2],lm[i],image_ids,edge)\n",
    "            image_points = np.vstack((image_points,contour_2D))\n",
    "            model_points = np.vstack((model_points,contour_3D))\n",
    "            model_ids_new = np.hstack((model_ids_new,model_ids_add))\n",
    "\n",
    "            MV,affine,scale = solveCamera(model_points,image_points)\n",
    "            affines2.append(affine)\n",
    "            MVs2.append(MV);scales2.append(scale)\n",
    "            model_ids_new2.append(model_ids_new.tolist()); image_points2.append(image_points)\n",
    "        affines2 = np.stack(affines2,axis=0)\n",
    "        \n",
    "        # optimize shape\n",
    "        ids['model_sha_ids'] = [item for sublist in model_ids_new2 for item in sublist]\n",
    "        obj_current = obj_base + np.dot(models['w_exp'][...,:exp_coeffs_to_fit],exp_coef)\n",
    "        _, shape_coef = solvePCA(obj_current,affines2,models['w_shp'],image_points2,ids,MODE='sha',num_coeffs_to_fit=sha_coeffs_to_fit)\n",
    "        \n",
    "        # optimize expression\n",
    "        obj_current = obj_base + np.dot(models['w_shp'][...,:sha_coeffs_to_fit],shape_coef)\n",
    "        vertice,exp_coef = solvePCA(obj_current,affines2,models['w_exp'],lm[:,ids['image_exp_ids']],ids,MODE='exp',num_coeffs_to_fit=exp_coeffs_to_fit)\n",
    "    \n",
    "        MVs,affines,scales = MVs2.copy(),affines2.copy(),scales2.copy()\n",
    "        mesh.vertices = vertice.reshape((-1,3))\n",
    "    \n",
    "    return MVs,affines2,scales,image_points2,model_ids_new2,mesh"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# load model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.io as sio\n",
    "import pickle\n",
    "\n",
    "def _get_suffix(filename):\n",
    "    \"\"\"a.jpg -> jpg\"\"\"\n",
    "    pos = filename.rfind('.')\n",
    "    if pos == -1:\n",
    "        return ''\n",
    "    return filename[pos + 1:]\n",
    "\n",
    "def _load(fp):\n",
    "    suffix = _get_suffix(fp)\n",
    "    if suffix == 'npy':\n",
    "        return np.load(fp)\n",
    "    elif suffix == 'pkl':\n",
    "        return pickle.load(open(fp, 'rb'))\n",
    "    \n",
    "def build_DDFA():\n",
    "    DDFA = {}\n",
    "    d = './DDFA/'\n",
    "    ourModel = sio.loadmat(osp.join(d,'ourModel.mat'))\n",
    "    verticeMask = np.repeat(ourModel['verticeMask'],3,axis=1).reshape((-1))>0\n",
    "    DDFA['tri'] = np.load(osp.join(d,'faces_1216.npy'))[[2,1,0]]\n",
    "    DDFA['uv'] = np.load(osp.join(d,'uvs.npy')).T\n",
    "\n",
    "    w_shp = _load(osp.join(d, 'w_shp_sim.npy'))\n",
    "    w_exp = _load(osp.join(d, 'w_exp_sim.npy'))  # simplified version\n",
    "    meta = _load(osp.join(d, 'param_whitening.pkl'))\n",
    "    # param_mean and param_std are used for re-whitening\n",
    "    param_mean = meta.get('param_mean').reshape((-1,1))\n",
    "    param_std = meta.get('param_std').reshape((1,-1))\n",
    "\n",
    "    # ours\n",
    "    u_shp = _load(osp.join(d, 'u_shp2.npy'))\n",
    "    u_exp = _load(osp.join(d, 'u_exp.npy'))\n",
    "    keypoints = _load(osp.join(d, 'keypoints_sim3.npy'))\n",
    "    w_shp,w_exp,u_exp = w_shp[verticeMask],w_exp[verticeMask],u_exp[verticeMask]\n",
    "    u = u_shp + u_exp + w_shp@param_mean[12:52] + w_exp@param_mean[52:]\n",
    "    w_shp,w_exp = w_shp*param_std[:,12:52],w_exp*param_std[:,52:]\n",
    "\n",
    "    DDFA['u'],DDFA['w_shp'],DDFA['w_exp'] = u/1e4,w_shp/1e4, w_exp/1e4\n",
    "    DDFA['param_mean'],DDFA['param_std'] = param_mean,param_std\n",
    "    DDFA['keypoints'],DDFA['verticeMask'] = keypoints,verticeMask\n",
    "\n",
    "    return DDFA\n",
    "\n",
    "\n",
    "def fitOccludingContour(mesh,angle,MV,affine,lm,image_ids,edge):\n",
    "    if angle[1]>= 0:\n",
    "        contour = lm[image_ids[1]]\n",
    "    else:\n",
    "        contour = lm[image_ids[0]]\n",
    "        \n",
    "    return findOcclusionEdge(mesh,MV,affine,contour,edge)\n",
    "\n",
    "\n",
    "def build_ids(keypoints):\n",
    "    keypoints = keypoints.reshape(-1)\n",
    "    # for expression\n",
    "    image_mouse_ids = np.array(range(48,68))\n",
    "    model_mouse_ids = keypoints[image_mouse_ids]\n",
    "    image_eye_ids   = np.array(range(36,48))\n",
    "    model_eye_ids   = keypoints[image_eye_ids]\n",
    "    image_exp_ids   = np.hstack((image_mouse_ids,image_eye_ids))\n",
    "    model_exp_ids   = np.hstack((model_mouse_ids,model_eye_ids))\n",
    "\n",
    "    # for shape\n",
    "    image_5point_ids = np.hstack((np.array([30,8]),np.array(range(36,48))))\n",
    "    model_5point_ids = keypoints[image_5point_ids]\n",
    "    image_5point_ids,model_5point_ids = np.hstack((image_5point_ids,image_mouse_ids)),np.hstack((model_5point_ids,model_mouse_ids))\n",
    "\n",
    "    image_ids = np.array([[0,1,2,3,4,5,6,7],[9,10,11,12,13,14,15,16]])\n",
    "    model_ids = keypoints[image_ids] # right left\n",
    "    \n",
    "    ids = {'image_5point_ids':image_5point_ids,'model_5point_ids':model_5point_ids,'model_ids':model_ids,'image_ids':image_ids, \\\n",
    "           'image_exp_ids':image_exp_ids, 'model_exp_ids':model_exp_ids}\n",
    "    \n",
    "    return ids\n",
    "\n",
    "def build_BFM(filename):\n",
    "    models = loadmat(filename)\n",
    "    sigma_exp,sigma = models['sigma_exp'],models['sigma']\n",
    "    models['w_shp'] = models['w']*np.repeat(sigma,models['w'].shape[0],axis=1).T\n",
    "    models['w_exp'] = models['w_exp']*np.repeat(sigma_exp,models['w_exp'].shape[0],axis=1).T\n",
    "\n",
    "    return models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "unable to load materials from: ./UV.obj.mtl\n",
      "specified material (material_0)  not loaded!\n"
     ]
    }
   ],
   "source": [
    "shape_predictor = 'E:/mesh2face/shape_predictor_68_face_landmarks_dlib.dat'\n",
    "detector = dlib.get_frontal_face_detector()\n",
    "predictor = dlib.shape_predictor(shape_predictor)\n",
    "\n",
    "CameraMode = 'ortho'\n",
    "\n",
    "# BFM face model\n",
    "mesh = trimesh.load('./BFM/template.obj',process=False)\n",
    "edge = buildEdgeTopology(mesh)\n",
    "models = build_BFM('./BFM/Model_Shape_crop.mat')\n",
    "ids = build_ids(models['keypoints'])\n",
    "contourMask = models['contourMask'].reshape(-1)\n",
    "obj_base = models['mu']\n",
    "\n",
    "\n",
    "scale = 1000\n",
    "models['w_exp'],models['w_shp'], obj_base = models['w_exp']/scale,models['w_shp']/scale, obj_base/scale"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# shape from multi-view"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MFgS8fG2fQRRgu4bnXrrBzrU9jBDcvfOQw8PHdD18983f5/f/8X/ItZde4/hsCirgW6++jg4ClFT01nhClJCfyXQ98cx++ve/5dr7OvAbX0p8E/CkLnz6on8SGL46mH56vJTSp/9FQRxFqCTl9OgxZ48e8tN33+bWx+8xn5/TdC2rskbIAEREZwymM7RNhektnXW0beebiUIgrMPZHrdmK2ql0EGI6Q1N13I+m7O5OSbSiqZpKcuatjOoNatRK18nOwChwPVIrZBKIpVn+OlAo5VCScEwTRFiPS5tW5SENItRWtFULVEY0DQNUkm2t7c5Ojom0Jq+7ynynDRJGI7GtJ3hfDanbjs6a4kDP33I85zz83NM1zEcTnAOTo5P6fuOzc1NQh0QxxGhDqnrxk9M4pjRaEhVlpyfT5FS4NyCLMuIopDRcEgYBrRdTV7kpKFgd2tIoDXLoqMqKprecXx+QRYoklBxejrFKsnO1R1euHGNNIqYXSw5f3TI3Y8+4oWXXmVne5PjsynzxZyt7W3Ajy+tdYgvExe4pzLLr1gv31Q8Cwy/BJ4EiSeR3zlP5W3qGq0UW1tbbGxsYNqW2x9+yCfvv0siHbd/+i7Hxw+puppFUTKebGGsQOnITyTahtr45mE6HBOk6br+h7KpqZt6fUcSVE2DKUq0Vp4hWBQsViXDYUQYhkRJTG/86NJaS920OPwYczjI0FoxGGREUYTANzH73noGoRYMhkOuPfccJ48PibSiMy1n52dEUUg2GPgSyzq+/a1vc+vWXUIVkJclTdvy4osvkiQJ87lvdE5nU3+eQUBtDB/dus3J8THXr18nkopPbt+lqhrqYsWVKzsMh0NW+YLZxRlKBexfPSBJ/Kj24mKGs/DCCy8ipWS1WnF2doJAMNkYMRwOqeua1XLF/tYmYRixKGpKo7h195A7tw/JVzmbGwPiQFHRgZREOqDuKpSDF69eZZXXtBdzPnj7b3j5u9/jxeef42x2wWA4QMUZgQ4RQn6mnHxqhVyuky+D50F8M4PDs8DwFXg6K3j6i/18tnDZbxCesx+GKa7vCYMQKX0XPV8uuZid8eDuLYrpGcdHj8jLErQiSoZ0vfAMPpvTdgbT9wTKz8x7Z/283Pqa2zmLlIq8qAjCABVojLVYQClJHHvNQd21yCBEhwG9tTSN5yzEaYyzftxobE++LKibFqxFS3XJiQi0piwq7t+7x2Tsacmh1iRpjFL+s2bZgLpt2drcwbSG1WJJGid0bUeaZEih+eDDj7h58yVOz8/oeotFoJRgOBoRpSnPP/8CxhhOzs45uHqFw8NDrl/dQ0rJxXRGGGl2trYRQrFcLjk7O2O1XBJGkQ8cyyVVWRMGEUmS0XUdWkdYC0GQcP3gJcqLKauzGVVdcTJbsj3chBcOODw95bwtCZ1CG0lRlNSLJeEgQiB4ePcOaTbm3Bxx8MoruN6As75kMR2Rs2g0Qq3ZkF+yZr4JpfovgmeB4WfgSTbw+S/XuX7dmJSXTUqcFwgVZUWgA8bjCdOjI44fPEDiuH/nE87Pjlicn3IxXyC0xLSGqrc09RJjDEmaIaVAIQiiEADT95jerJuJnsYchAGD4QBjDFVV0V6KpixBGCCkoix7QtUSJF6g5GL/XGP84va9gvCyidbUNUVRMBgMfBDqe6xzNF3D44eHvPG973D44C5KCjY3N2nqiqoskCrg4MpV3n3/fTY3N8lXJdY6wijk7Xc/5ODgCmVZUKxWDIYjTN8yHk34we/+gLu37nD7zm1603PjYJ+zsyld2zJbLDk5PyNfztnb2+batWs8//zzHFy7xnQ64/Fjz7CP44QsG9C2LUVRrIVbvrTx/3U4J9BOEYYxQgh2NxSrxrCzNebw/IT5smJ3Z4AMQhZlxXS+4vmtCa43PHfjgHfe/RCnYyZ3bzPc3OLmt8ckcURvLXJNHsN+2SjyU3xlL8GJr8g0fv34RgaGb8II52mtgZTSMwgBYzqkcOtpQ4sSGiEEUkUoHWCrltOjI975i7/i9N5tZtMzHhze4/DwHkW+IC9KkvGYJE2RQUBrDM5Zojj2DcNlTt00OPz7B1oQBCFdJ2gbQ2c6TNei1nV8pCXjvW06Y6jr2qsBhSQaK3pjaaqKLMtI4ojVKkdLSddZT7jqDErI9Z3OEcSaqi6YDIZoJQmjAbZt0EKSxTHCgUQSqoAoC6nqmqbtOD87Y5hlHB+fYrqG3hrOzxfcfOGA4XDEg4cPCbUklIKbz71IGCp++t47rC5W3LxxgzRJqOuaJI59cC1yyqpABwlOpRRVz+0797l/7x5JnDIaDImiaM069GVRFEU4LBcXF5yfVwig63uU0gyzAa1TlLVFxQkbA0/C+o//4Zt0vePx8SlnpxcUYc9sUVIaw5vffY22XPHdN15jusw5PrxHnGaMNzYZX91ncTElyWLSMEOqECz0P2MdfRn8P8unHj+dcfzdKi8/j29kYPgm4Aml+Ak+04CU/q7d9SUOX4+nSUqe59z96Kfc//inxFKiA8nF/JxHj+4zGCQkScBg3NFbhzE9TdeuGYKWtmuZzi4o8hIHhHHiF4aEpm6IoogkTXFlSV3XtF3rs4cgwPYGJSCJIy8VdtC2DTpQmN5iTEeWZGRpQts0BHGIdb7n4DkBEmctxrZgn0iqBXEUEWYpG5MxXdeytbVFGGjUuuRI1oQpTxeWDIdD8qKk73viJCEMgk/7ITjSNAYcjx49Il8uiHVMVZfY3jAcZEilqOqCJAmJU02gAnBeVDUeDRhmEWXp+Qmj4YgwDFFK4ZxFacVgMAIgSRLOz86YX8wRUlJXNYPBgCCOkUqQJDFxEq8Dc8/iYsFhXdO1PcFowOHxKeNBwgvXr1A2JZtbY85XHfnqgsXsnI0r+1jbU+QF6cbgUj7+q7yQf7XD9789ngWGn4FLLwOejuYCpQLKqiGMInCWvuso8ws+ef8D3v3xW+xtbpBEIbktSMYZu/t7NFVBtapYLn3W0BtL7xxIQdM1GNNT1zVCSuI4RsqAtjeXk4murJF1t2b8BTjjS4EgCCjLEmstQRAQRRFRFFEAZVnTtcbX/GFMliQIZ73/g9Q0df3U3Za1IEp74s2aXj0cpAwGXjgVak0URnRd58ebQUCWpWit6XsfZMIoJHUOqRTz+Zy66whj3w/QQcCDw0NWyzlSCqyxGNMSRTFJokmimDDwFGcEJHFC07Q8OjxhenrMyy+/wObmJnEco1VwKSsXQlz2dHZ3dz1VvOvonaUsK+q6IgwCxuMxSZrQtA2LxZzeOYxzbG9NWC62WSwLbG9oW8Hdew+5dtVrNqq2RmtJU+WcHB1y7eZrxEFE13T01hJ8DVfRrztnfhYYvgJPzEo+34SUSvmUUQqqMsfZjjgM+Ou/+gse3b3Pd1+9yXQ65Yd/+a8x1GxvjogzzbvvfIw1DicUSoeEkabrDbPZzI8oTYcKAqIoRkpF2xrarqNsO4zr6bsepZWnLdt+nXVY4kiyMZ5cXgz5csWSnDAMyZKUQSppu5Z85cd5m5MxJyenJGlC2zqs6bGBpa4boAfl5/Ft29I0DXo8pGlqijynKAq0kmRJTNvUVHVJQspkssl8Pve8CeuVh01dc7FckmWevNS0HY+PHntJtw7RWmOtIYwjolBhbUsUJGRJShRq5vML8uWU/asHvHj9gPlyycXFkk8++YQwDHnxxZtEUYhzjtWy8BnN9talv8T+/j5hGFDXlT9mVTA9O/Uj0DAgUpqm7zBNR91VXL2yRW8a8lVFkA0wQvPeBx/ye//gDQaTAXceHBKFEavpKaePHzHc2iMdeM5IJz/PfPz58PkG5WdK6F+zPPtZYPgKPOEjOBxSeEMQzyKUVG2LUpJVnjNII4rVknt3bnNle5f54oK//Ou/wmnHf/YHf0BbLPh3P/whgdYMJmPiJOV8OmOxWlHXNVVd07YGHUjiIMBaS1XVFEXjJdZBCNaiAq9dyIucMAi9A4Bz2N5SdzV17UVRcZLQtN6YJQzD9TlDv+5BBIHnBXRte/kavskK1oLpDEYbbNdRrp2L6rrmfOoZlculpSxy/0uy7tL7wY86O9/IbDvazmsplFKsVjln5+fUdc3e3h6d6UBYTGURUjEeb3Dt4ApRqFku5yglef7Gdeq6ZXo+pVrl6CDiueee4+zsjAcPHnDrk0/Y2d1hb2+P8XhM3dQcHR3R256tzS0mk4nPdJSfDDnn2Z0nJ8dMxmOyQcZmllGFNWVdE8YJTVlwMb1g3jukANqCW7fu8sYbrzMeJlRVQSBB4eiamkGaARbwGdgvOoD48gnG38Lj4WvAM/v4fw+eSGwVDnpD2zZY09PWLWkcg7U8PnxAmsaIQPDRnVsk4wHf+s7rNFXNj/7yr/ngnfdRUtJ2HfPFguVySdu0CKGI44QkickGg09NUwRr1aOvn5MkJksTlJQ0tU+fx+MxQgjOp0su5jmd6QEJTjDIRmgdUlUNZVnRdR2B9kzGYrViNBpeKhS7rvN6ikAjhe+tqHXPoa59f2BzcxNnLb3paJuGsiwxXQdY6qaiLHNM70shL+Yy2HVp0feO6WyGMYYsy6jqmqpqqStDlg2Jo5Sm6Tg9OaM3Pc9fv05VFBSrgvFwxP6VfZI4BQdaK65e3eP1b38LHWhOTk7I85wkS7hy9QpSSrq2YzqbcXR0hLWO7e1t318INHEc0xvLdDpjNp35gOYc2J5QSV547jp7O1soLXwAjlNOTs45OjpmZ3MbpQTlak7fVihnMU1N27b07iv40L8Mfs1jzt/6jOFpgtLTj4WQPLE+lPi7oTEdZdkQJRm2NUjX86O3/ooP3n+HV169ybsfvsv1Gy/y0saEF288T30xQ/cBrgFnJfP5nKKofDmhNKbvyfOaMPaZyBNXpTCM6IylrGsCpWhNR1v34BybGxlSKqqqQinFYJh4QpLzI8a6bambDiElpusIA+2Zgb3XTQShJghCdnf3uH//IauiIgwjjOlo28aXIIMBCjBr+7hhNkCKnnq1QmkfvKqyQmpFqDVd2yKlJ0ct8py6bqiahqbt6K03a0lT72mQT+dEUcR4PKFrOxonSIKMpqg5fnREIOH1b73O2ekZ9+7eZzLe5ODgOgjobE+eF8RxzD/9J/+Eqq65/+A+Dx88YDyZ8OLNm8ymUy4uLjg7O2M6PWdnZ4dXXnmZ4XDI6dEpG5MN6qqiqiru3L7D7s4OWZxwfnJElmW8/vrLvPPBxyznSzpjGIUjfvrhHcIgYmeyyXxVcvTgLq9t7VCVK6LRCKmEpz+7p+6z7sn/3M81ZfvC5OLX3GT4rcgYnk7VPk9xfvoLsU/93Fo/pnTOPXU37InjmPFgAM5y+9YtHj96TJImCKV4/Xvf4+Vvvcbu7hUmwzH5bMX56ZS67siLAtMbgjgiSRNPGS4KjHEkSYZeE5oc0JkOqRTDwYA4DAi1ZjzMGA0zpABne9q2+bTnYQy2ByEUXdsjpVpLsiV13WCMIU4zjHXoMPI07SRhY3MT039K4bbO+ElEqBmNR2xtbSKl4M6d2yzmc7/IAS29A9RqtaIqS6q6Whu0Bt7IRUjkenJRVRVRFHljmK4lTSOyLEU4R98Z6qpkPp2uJd6K+cUc21teeeUV3nzz+1gHq1WBc4LetGgpEc6xWM7JspTvvP4dXnvtNSbjMVIIhsMBk8kGk8mE4XDE2fmUo6MTsnSIlJKyLAHhg2/bczGb05ueKAzp2po41Lz66k2yLKJpG4Igpul6ptMFxljGoyHL+YxieYHpaqzzzlh87uJ3T/78nHf+J3T7T/U4v97I8FufMXwGn280SumbV2suA8BoNGB6dsrRo4ccPnqIdY5XXvk2+zd8g0zpiEyHdFXL/dt3+OTjjzmZniEDiVQKqSWm8x6NSZKwsRmjw5CmqenaDim9D2Pb+Tq9LL2ZidCSQCsIInpjMC00TY1Y8x2kdJc+AVJKOusNXHSg1gvUTy3qqubg4ICz01OSJCVQUBUFQoLrLWVR0mYpYeC9GrSUuL7DWYVTYEyPFBBGIbJXfsRY1UjZXhrQtm1H13VrYZdeTzocQnjSVttWGBpC3HqsqbG9oapK2q7k0aNHOOc4OLhGlo14+PCQ6fScdOAdpYqyoCxKxuMJo/F47Q/6dgVfAAAgAElEQVSZslgs1tnQDloriqKg7Qwffvghk8mEvZ0dyqKkX/tGbm9v0dQ1s9mc3d0thHS0XcP+1R20eJ13fvQ3GGPY3NxmtSwYDgq2dzLCYUrXVFTlinhjA7mW3n8zqUq/GH7rA4MnyXwqnX6a7VjVNViL7b05RxzHnJ+d8OD+LcpiRWNa3vj+D9jc3GI6nXN1+3mkhJ3hgA/feov/43//35henBKkAUJJoiCidwJjGoIoXAeeGrc2WvF2646mqcnzCiFgPPTCoN729F3rm6ACJuMhOEEQhuCgLCuquqZuWjrj0MHatHVtXlpVJUpppFQsV8tLA5b9vR2KskAHkkpLpLVIIaiKnMIYRklMqBRd2xLqGIejNYZBNsTYnlWR09fVWifSEwSatu08FyMMCMMQnpi5Bp4k5qwjiSJiIciiiFCB1pI0iYijANt13L71CR9/9BHPPfcC+1d3uXp1l/lyTtd1xHFIU7ccPX7M6ekJcZz69wGiKCJNU0+XXq3ILnwzczqd8vDhQ/avXiXQoS+76gYtNSrUPHr0mDgO2dzZQFrDzReu0yxzcIKmaZmMBlzM5uAE14YbdE2J6RpM02A6g1zb3f19wW99YAD8aOipcP8p41F5ookQRJHv5J+eHbO3t83HH59z/bnrHFy/wXy+QqmIUTamXC2plzn/5l//OW1XsbO3zbJaESUpfWdpm/XFrfRlmmlM60k6a3allJIw9ESi4Im8t+v8AlQCKQWB8kGka2vfHJWOJNaMRxnLZYEVlq7zrEpnn5RIXmdR5itCrSmUYnMyoe9qkJClKaZpiOOQNAqpisJnAa0FegR2nUkJb0Pv6xfk2kimaVrPLbAWJQQ6DAlC7xCFc2tDE0iylDgMCXGkcUoWB2wOM7IkwnQNwllGwwwlA+IoxHQNUmvSJIY0xRjDcrlCK42QXkMRxwms6/m+75lMNtjY3KTrLQ8OH7J39Sqr5Zy2axChoG4a6qpCK0WWpiRJQtv5izyQGvqeK3s7TKczemepmgrT+KxosLXD+OpV4lD7z103aBVd7mPx9wG/9YHBax4+228QQqC1vjQ70Vpj+548z6mqkmESsFrlvPrtNxBKkZcloQzoTUu5nPPDv/hz3nvvbZI0xvQdrOf6y1VO0xjiOKHrLE3TobWEzi9oa9Ul4zKOIsIoJAwTeuPT8ieVp+8zGO9eZDpCHRCotcgpiYnjiKIsqeqG3lra1lw6T1lrsV2HMx211kTbm6RxxHy5INSKUCZ0bctoMCTUijiOoPdBoW5qnLWMRiOcczRtS2cMsfZj1r7vsWuvSa09YUloRdPU68/Vr52sDU0QEDqH6Ho0A8LNTbIso6kFfd+xtbFBFCae8BWGOAGrVUWUpIxGI4wx1FWDs47JZIIQgjzPfa/EWpqmJopi9vZ2ef/D9zmfnjEZDX0wCBQJkd+7ovdGu2k6IHEJWDg/9ftbKOFQGvJlTqcDsijBCUFVVTgcWkl663wp8Q2g8f8q8VsfGOCzevmn1ZJVVZElMYHW5GuCT5YOefTonOFgi7oy3Lr9PsvVkpeev461K6Znd/ijP/k/cVhWVY50ilE8IW+8rVoYeJu1VVGitGQ4HLNYXqC1pm0buq5FSIHSMdbCqijQa1VkHI5p24qubYgDhZKaIByQZRlSCLquoyhLRoMRYeD3mAi0N5CtOx+gXG+xSnqaY2+oy5wrO1sIYTk8OWJ7Y5siL4iDkCSKSJOENI4QWMoyRwBbW9vMLmY0ay5E3/v+xWCQYXoLeGNZKSWmbenbDmEtDkvvPGGi71vq1tAVLauLBauLOZvjIbvbmzz33D4aTaj8TlbnJydE67t631uqsmR3dxcpFOfnM8y6eZplGWVZcnx8zMbGBkEYEicp3/3ud3nnnbfJ8xxcT54btjd3UFIxn82ZzWawucFoNCBfrmhLQ6wj4lRxcG2PcKbIVyXGddB5A575xQXD+ZzBzhixFtM96zH8huLz/glf/Pmn/ceiKNDap/LO+nGgMYYsHfLeT97jypUrBEozHGaoQKClY3rymAf37mL6hsVyTtd3DNMUax2m83N8rTXz+RJjetJsQJIOCNYqx7LOidLU8xc6P7Wo6xqsIAgkDFKyNGEySBkOMprWTxyq0usTnIOuM/R5jpCCMi/QgTeBTYII6yxBEiCcYzWfI4AiL8jSlN3dbR4+PqKsapIwJAxCcI6yqDBNg5KWyXjEaDRkMBgynZ4TBQFRktC1BpQGBFoBTtFbi1jLxJ9kCs7ZNXcjxrQ9OvbBJ5SSomnoZgt6JEEcMxik7GwKhtmAzfEmRV1SFTlRkmJdz2w2I44SRqMh1jlWRc5wOCTNMsq64v79+wgpGQ4G3HjuGq/cfInjx4+o6orDw0dMz2a88b03GAyG3Lt3j9VqhXWOQCmEkuRFzt7VGxR1yXi0QVv3NGVNGIXYvqNaLckXCyZ7Eq2Dzy4e5y7pSU6s/Vp+vZqovzW+GYFB/PKKyp/HWfrJpOHpEZKUYK25bNL5ZqSg6xqGw6EnsBjDxWzG2dkZk8mEII64+fKLRIFmtnJsbE+499NbmOWSj9//KcY4prMLkjjDrGf+bdfQzRqWyxV1C1ZIOguoCFM3LJdL0lGCVJLOGERv0NKxtzkiTSIkAqUkw2yIVoqm7igWuRcwBYG3h1tbv5mmI8v82O70dMFifkGShN5uPZXs7e0SKm/11vc9J8fH7B1c4frBHnfuPEJvbnjfwyjGaT+erMocl6W+EVlXbG9tEsWJl31rz7QsT3PU2gHKmJ62N4i1/2TX+1Fvmg2QvaMoVhDGLNoeZy1age0Nh2dTTi9mPH9wwL27D9kaDrh+fd+brwxS8qbxvphh5CXp6wCuBJydn6CjiGvXrxEEivl0RlMWPH5wyGAwYDQYsrhYsL93lcViyeHhIdeuXePmKy/x4x+9xWy55OYLz2GxNF3L/YeP2draQjhLEiYoJ4h0wOz0iDCKiIabXH3R+NHsmj0q1/tdCiFwUq3Zs9KvcQcS753hhAX3RSPhbwq+GYHh1wjn/KalZi3GUUrSth3D4fByvr9cN+Gu7O2xXC25/twNdBCQrxa0dU1nOyaDAe++/yEnx8c8OnyEVAF121LVPQJN7yTOggxiBkmE0iFCSi7OT+naliQOkb1BOUeqJYNBQqSHpGmK1mvrtrZluVyR5wWzxZIwCr0Yqqrx4i5NFCl60/Pg7JCdvT1eeOEaZVnz6PEpZdlSVh1d2xNq7zCVRAF5sWJxMWeSZQwTRagk1hjydkUoBdnWBNs3OFhv/5aiVEBvDFma0tQzpJBY05KlMb219KYhjkKWqwKt9PpO7ycu0lmSLECFAb11tK2jtRapJEIEHF8sOTmdIfqe5w72mc4Lru5tsHtlzGA8Ic0yjIXhcEAgNU3Xoo1kvnIsLi7Il0s2RhMm2Yj5/ILT01PKsiRLvSBsNrsgikLOzk55+PgRL7/0Mt/5znd48OABp+fnZGlIFASkpPSmx/WONEmQ+KbqIE0o8wWr2RTbNfSmQYUBT+zg5XpXMNtbUOIygxBOfJo2uF8/V+Fn4bc+MHgbePHUmNLTgp/smeDwOgPwuzo3TUOSpUghqMt6zUkw1Is5UkJZFjgHURSzXBWAIEkCAun9A3oRILRGSYl0DqslwsIgDUkCzSCJGKYJsfIpahDE1K2hqhuKsqJqO5zWpOOxd2ZqWy/qco5+TW/OkpQuNuR5gXUQBAEbGyPa1tA0LYvlCpC0xnCwf4XhcEQg4WBvh9OjY7quIQ4Dv7WcsHSm9dmIVH5vhd4Sx/Elb0EI4XeZFoIwCOiM8WrMKGSV52gt1/tXdDjXIyXeWWrNr0B6XwW7npwI/ERDC8G8rHFiDqInSZTXJCDRQUCd53TSp/6DQcYuuwR6Ttf19G2PwLtMZWnBcrXEOcd44nfwquuaMAy4WC65d/8+P/jdH3Dt2jXu3P7Y60icpW1qirXRbFn4EawKJJHSlK3vz3RNiWlrwiRde2h6dyvWjltSaZxw6+zgMwvvm+rqBjwLDN4+7akt432t/oQNKOhbr1oMw2Dd1fez+jRJuLdYsVzOqdoKmRcMRxllmRMEGucEQijSNCOOM/KyXouLDLQtSoCWEAaCne1dmqpga5iyPR4TSInBoHVEVRvqztAYS2sdBoFT2nMirPVd8nJNdMKxuCixnSNNE7reMw/btiMIIrJsQLXeMbrIK45PLwDHaDhgZzJmb3uH56/d4M79ewjbk6QJzvVEUUDXhWjluRdN3VxOJozpyLKUoiiIAo1Y6z0CrYnDcC0Mc0RaIqzyLtf4bfps62neSgrs2jVKSu93AaCCiM455kWBcC3jQUjXW4QOuba1hTE9eb7CCfyWfg4GwyFKBhSLFat85dmmUQiFIC8KpFakWYYOA5TWfneupuHunTtcvXqFvd1d6ipH4LiYzvw2gVojlQ901hp622OtoWkqlvMLkospKoqJ0tGa7yhASJz105wvJz/+ekVS/z781gcG8OQiteYVdF3n9wroOqIoZlUtyIuC/atXUNLf+faveG/C2fkZSZqQJQnZeMi/+J/+Rw4fPyQbjegsRFGKDmOWxYrj0zOQmtFwQCggDiTDQcKVvW1OT0+YDGJ+51vfQiE4m55z99EJZ/M5goDpbEXd9kgNg9GAIIyo6gKlNEkUkSQKrCOJY8JtzeHhIc4Zut6htCbQgXedNi1hEHD16hXapmI+n3MxXzCbLTl79BhRtzz/4nM4LEcnj7G9pGkNafwiWgroDYFKqZqGQPkdrayxJMMMJSx1Ea29EfxExDnDKIuR0hPIdKQIwhBrvbNyhBeEdX1P3rVYCXEcUlcVQZwyHGY0tddwzOuatz/4CCkl4/GQ17/9Lb71rde4euUKZ7Mp09k5Gzu7hFJzejYlDlL2r10nL5Y0TU06yHj8+DH37t9nMpmwublJ3TRsbW5ycnLCwwf3aeqKvd0tAqW9AnW9L+jp6Sm7V3bZvbJHvlpyfvwYgyJfLVicnzAYbxKmQ8JkhJDC+2wICWJ9w8F5FfVTKYO77C58M4PDb31geJItSCnojEMgCcMAZyymaWm7hjAM1nTiCoDpdMrt25+wNZqgQk2SxTz48B3ee+9dkiTB9I6iqqlNj25b+t4gAi/KUsoibUcSJUSBYH5+SigF3//u9wiDiE9u3ebe48c8WM5ZFQZTgVIQxZIw9IrJ+dxLr42paAJNlmXQO7rOsLu9TdvWCCFYFTl5UWE6Q5qlSOFw1oD1e0QOBym27/w8H8EHP73FbLXgpZvXGaYpZZlz42Cf5cWU7e1t2rohDhWBjokjhXOSzjjaqgRnydJ47WQkCaRisVyQxTFaee8JpbylWqhDPw6uPQ+iaRpEFBKE2u/c3XcMU6/+PDs7xwGBdqRakCUx87ziRz9+i6Io+L3f+102NzeIswijJALNZDJmej6nqAuvelWSqqnZ3tlmsjHhwf0H3Lt3l8l4g0VxwXCYMZ1OmV/MuX5wgLCgBYy3t1nkq7UZTkVd12RpSpmmGCcxTUVXV2iJ36PDgVTae2soibQKrM/l5HonKvsbIk/6exMYvLPWF8UsPEVx/vyGID7pc5ein66t13ZpEY2pWK2WNHXNeDzC9obz81O6ruFiPqWuKkojSAYJ450N/t//7098CSEERVlStwalI6zt6fuWYRIwSBOiQKGdYphGJKHmxtUb7O8fcHR8yp/96C+ZLnJK46isROuQwdB/BmsdTd1h7ZP2liCOveV72zaeRNXV3H1QsDUZ4qzl4Mo2VbX2augNwgnSKMGZmkBAmkXsDvc8Wak2CKFom4LZ2QnX9/fQYo98uaRuW5IrOxhnKFdzwjhEip7hKCFNE/I8pzeKJI5RSlLkBZEWBMEEgCCMqera9xOEJE1TnIXFckHXttSNQI5TkIKyqhAupapKqrojHaToIERgCZTE9IYkiBjEMecnU37y47fRoeLq9au88PJLBHGCBKL9XRaLJccnJ5dCLuf83X80GlLXfkqzu7vLfD7nzTff4PT4lN70xGF8uZtW0zZ0pufqZIPFYkWWRFy5sk9e1pxfLFjMpjR5TjxqcNYitPIlp/Mpgpd2f3G9fnPbjh6/cYHhKz36nfuCT94T/YP8XGC4VFtah1ASIbwk2RjjBUtCUlXVurEIgVI0dYXpOkxvyMuc0XhI0MHGaExVlnz80ccgBXlZYoUfU1ksgdJgDRvDlEBJkiggUooo0P8/e2/2Y1mWnff99nCGO9+YM3IeqrKGzmp2N1kts1sUmxIJibLBhmRLhgwKli1AMGw/GTCsv8DQqw0DtmXAsPVi+cGGLFGTadkkpRbJntjVNWdl5ZwZmTHeG3c6495+2PuceyKyqqu6WewqlrgLtyLiDufuc/Lstdf61lrfx+baGhfPXeDWe7d58733mGQZqZQsypxWGOH0q42jQfN3l1LKV2Y6HYnA34h5nrp+BCk4PNij32kR9zoEMqIVyuoiEShNt9tx3Y1l6WsNLLHSGKGIhm0oFuSLGSvr63TDkMlkynw6ZX19jbws0KFGBp7mjZJet0ueu5TlynDI4cEh4/GY4WDIIk1ot7vMF3PSNCPwnZuL+YJQO0Ne5AVlWbrXlWNdFkislVjhUoGFcbG6zUuUyejoEBu4VGorjJiNJzy+94Dt8+cRZUk7Cok312m12/zgBz8gyzK6XSeek6YJ1lgmkymb6+v0+j2nRzEccv/+ffqdLlpLpFB02j2e7D6l2+3S7XaQUrO5ucniwUOEECSzKdPjI3ob2xR5glZVybjvrjRV3cxnlQ/6g8cfO8Pw44xTLRAf/B6oS4UrPgRTlq5oyFTlvYrR0YQ0XThOhmTOcxcvM3n4FInlnbfe4uDggFYrpPCqRNLH2q12jCkkrSDEljmxCoiDgI3VVS6dv8g7777HO2/f5GA6w3S7qFjREpq4cO3UMpAEkateLIqCIHBlz1HkiqIk0Ao12IiiKLBFio0kUajotEJMaShDTRQ48FQJyaDfJQoCpJDkaUqSJhSlReqAdhxBmSJsiRaWtc0NVldWOJ5OGa4MUWHALJkThBEIiZS27vEoiwIsDPp9l3Xx1YhxK6bTaZH5bEC/20EJQSeOaMWuBX0ymTCbzdFKoqVkusgQYk6aGrfIhHS7sbUYBPM0pcgzOgcR5+JtVoerTEdjHpYl5y9dohSQpznra6u8cuMGr732Gvfv3WP7zDapJ5vJ04zDw0N6vR77e/s19fxhXnDu7DZlaeh226ytrvLkyS5rayuYoovWIf1en+ksdTjD4SFrZ2cUWUKpFVJHKKUorfE0834j8nijbdx7n9XxuTYMH3dI6br/HJNRQJoukEp6q2FcC+90wtOnT+ivDLj+/PMcHxzR6bRoRQEP79+l0+6QFhk6jABnaHq9NkLitBxVzHCwzmw85uqlq6yurvHD197knfdvkRQlZRBQWosSikErZChdU1BCwSxLyIuc0pTYvMRSIEyMKXO0tQSq4wHQLr1uGysKZosZG+srxFGLNE18o48kkE5+LtSSUIeoToy1fYIwJGrHKCRaCIoiY7FYsJgvWFlb5eyFC+yPR6z2upzf3CRJUkdkU5RIIQHLbDrDmBIdBPT6fYSS7niem1EIwdHhEe2oRa/VJV0kKM8eNex0mM9npFnGwfiYyWzGcBYzPl4wTzKOpjMWaUaxSFkAcWsdHWoe7x/x7q1bbG6s8ef+7DeQCLJkgVGKLM0Yj4958aWX2T6zxXe+8x3efPNNlFKOlcpabGmZTmdcvfYcT58+ZW1jg/FoxOMnT9jechJ6mxubFEXJwd4+R3u7BNbw4hdept3u8f6DJyxmE0yaEEon+mMoEUI50l6nYecKmrCVat2nTen4keNzbRjsx8wIVWnKinMhy10K7unTp3Q7bZIkZb6Yo6RjMLbGcDQ65NrzL/D+zXd56403SPMcqxSlBUyJEC4MoDTEQUC/12N0dMSZ1XVWV9a49f5d7j54hI47ZDOvQFWUxDpABhGoAhlotAzIF8eUpcuWtOKIQCo6kUZaRSAsLSnQRY4uFC0JMoiItKDXajHo9TGmw2KeEEUBnVaHQCnKMqfICpQStDsdl4kpC2zpFKnCIEBKxWS+YHf/EKKIja0t0jQFY1lf2QAspnQcEgJL3OpS5A7JL4qCvCiwJQipkd7orq4q4iBEacVCz7Fl6SsXXTlymqVoLVlb6VEY2Ns/5HA0Jo4DHu8dMcNhIePZlMK2uHDuDIO1FY7399jd3WNtbZV7dx5w7spFOp0OQRBw/+5t1tbW+PLPfJH5dMKtW+/zdGeHwWCIsTCbTuivrHDuwgVu3rxJrz9gdHRAmicYUzCbTRkOB0hhWcyn3L9/n+HaKmtrm3TbjityfHSILXMirUi8nqWSygvbNqnfDCBrw2A/o27D59ow/Di5YqdipLHWUOQ57ThCa00QBIyPj0jSBTrQRGHIbD6n2+2SLOZ897vf4fbt9zEGX7nnC1ukIMtSAiXpdTos5nOG/T7XX7jOnXv3eO311zE6JBdgfEltO4poBSHSWqaLCTZTEAUURYGUgijQtDzDcawUgRLEShFLAaaEMqPIEgIZstrv02u16UQxYRBQtguElORZ5joxgxgRuFbvVquFUIJ5klCUOZ12lzCMyMqSTp4xSeZkRc7KyhBbWmbzBRIIwpA8d8ZUCEu7rdC67xmwDFmRky6cCI47B0Wn1/UeS0Cn7STl0mSBlhapFTqZo0KFtSB9r0qrHdPtdAlkwKjVIskKxtMZs8UCBLxw7SpaSsajCe1WGx1qRgeHbJw5w+rKCvcfPGA0GtHt9rh48SJZlvPo4SNmsyk6jMmyjDfefJMvf+lLbG1t8eTxDu1Oh7IsKdKMY63ZPneWPEtZzKbMF3Me3Lvr8Keog9aC6WTEZHREd7BCoEMWpUUYR9ar675Yj3FhPvNNVx9pGIQQ/zPw7wC71tob/rlV4H8HLgN3gb9qrT0SDmX5b4C/CMyBv2Gt/f4fzdQ/xviYV766kbV2VOtZmhIFGqUkabJgOj1mPB7T73UZrqyA1qjhCrde+wF7u08p8oIojglbbY5nrhkn1Ip+r00caGKpGbRbfPlLX+TgaMS3X38dGYegAuazBGMlWIUSrn6iLHJEKLAYZkcTVxnZiulFEW3pwMteFBIqRasV0I5DpHDz7/W6tKI23U6PuBURBoHLtLQdmYn1FZLKg5dF4dqhtQrY3hhS4uTcgiDESCBwwGeSppiyJI4j+u0e+SIHYQmC0LUeK9eqHkURSmkQIJXCFM4TmicOzA2iAEuO1pp21HLt7JMpWbogsIaOGJBnOUmWOi5LFdIfrpJOZ6y1uhyMxuwdjUEIpmnKk6dH7D/5ATeuX2D6ZA9lBHEr5HK/R5Gk0HHKVfP5nE6rzerKCje+8AUGgz7vvfeer47skCYjvvWvf5evfvWrXLpyiYPdp+zt7hAoTZan7O/v0+m0uHz5Crs7D3mys0Pc7nD5+hpZnpDOp4wODxhubtIerGOLZdhQ/6zvOPGZjyU+jsfwvwD/HfD3Gs/9beBfWGv/jhDib/u//yvgV4Hn/eNPAf+9//kpjSUr04e/w3p6NABBnmUuHsah19jS5aSlIAxD8rJksci5cuE8b0+mzCYTSltgVeAq4gqDsBBFASu9DhqBMpbLFy8wncz44etvgIpo9TqMpzOEkugShJZoqSiKEqQgbrXIi5K8WDhVKwSxDuhGAQHQjUKCQDpSlThCKUEUBQwHAwa9IcY4BiXtyVsFwhObSEwQIhVIqdH+2gQ6IG61sML1dFjl6vpLYwjCkJWVNotFghIKhaS92nMGIQq9cA5eDEf7YjHHAWmDEG0tKoqwpUWHGiMKX/GoCIVEh673IPEpzTiKCYoWFuj0+0ghyaYzAiHp9jv0+h0McHg8Za5C5tM5o/GYfhiSW8t6f8B4NCLutpnP56yurlAWBWVZEAUBtAzXrl7m4cMH7pyCiLjVJisMD+7f55UvvMTK6go7jx+T5xmDlRWmsylp6lrat8+eJb2bcnR0xFVj0DgG7jxLybNi2WSJ9ZXPpvYP6p6JP+6Vj9ba3xFCXD719DeBb/jf/1fgt3CG4ZvA37NuJf6eEGIohNi21u78OJP6STotT37GfmAZ6gcJfFRJTmNBWstitkBXMXMYkC5yrDGsrq4C8PDhI3qr22xsnuVo/5D79+4gpcFKV+yGMbRaEevrQ7QpEWVJv9dna/MMP/jBDznYP2J1bchovnBgmoFQBmghkZ7+PY7bBFJzPJ2BcdWACkE7COhGETbPiBQu6xFq4iggDELarRa9ds/F/AKiMEZpSVkWBGFIEEUoFSC1M3RSaXQQ+DJlR9EAFhU43kgjcMQu1oKBTsel9ZTU9bGsZ7kq8ty1desAY10zmtPXdB5QEMUEKnCeRaA967arrYhUhA4COsZgyoI8TzEep6k217zbJVkkxHFAHEhMktMSkl07Jg5ajCfHrFw4T2It7UGf/b2H9Bd9Mq8z0e12HLi4tkqeuwKw569f5a0338ViXSt4CePRMXdu32ZtdcjVq89z8713GR9PiOOQojCMxhOuXLlEVhbsPt0hnc9o91bIkwWz2YR0sfBzd9T/lhKFExGWljoNu7wpP8Q4nGiysj91D+MnxRi2qsVurd0RQmz6588BDxrve+ife8YwCCH+FvC3AM6fP/cTTuPDxklrXBU4GWNq4ZgqPRkGAXnmmn60EI5uXQju3LmDkJZuu8Xde3eYTWf0+j3OXbzMjS+/ysHTp7zxw9ewpiSIArAC5Uud11Z6dFsxLKasr6xy6eIl7j98yJ379xFCMZvNmS8SpFJkyYJur4WWktlshgwUw+GQ2WzO9Dij1wnpxC06gabXbjPstl0fQ6zr5qRer0c7jol0gACUkMhA1xJ2UrrOS6UVOgiIWk4IxgqJ0qrmgqyum1DOylmMJ17x9RPSFXAJIVFBgNSB7xIVDkYX7BcAACAASURBVH33hVgutFCu+1Pgi8yoiWuFEigdElhTI8RORt61LJuywPpUsTHu9yJOOXMuZ3oY0Io0sY5YX1vlzZs3OZxOwBbs7u7xdGcXawxf/vLLFLlhPh/zvW9/hxdfeokoCDjc30dpRVKkbG1sYF8SvPnOOxSFoT/os/dkxs7OE9I05eLFy3zta3+a23duMZ0cI4RlZTjk6OiIza1N4ihi/2CfgbHo3HA5jhkdHjDYOIPUbXctpStw8t38vjbj46Yqf/oGoRqfNPj4Qef7gWdmrf27wN8F+NKXfuYTPftnKPo/RArMWLMUSDGGvHQppUoodXI8oduKa7qy4XDI9vYZbJkzOthHepLTJEnQcQdbligpWF8ZEkeSLFecO3eO1bV1/t9/9RscjScEYUDiqdYM0O10GPT7LGYzlPJNV1HMeDQm1IJWGNLvtImVQgrXaBRqQdyKvFZlQByEBMppTkpcrB+EUc0arQON9r0gUkq0NxIWr5+hpGdydg1OUinXcYpFGePBVOV2QSGwCK9w5RmqccdQ/jXXiSlQStVKWJWBMI5HDxFIFNSGQWIxtnRq4ioA64hnisKXlEtLfzBEmpxACbQICNsxe+M1SmEoy4IyF0ync374w7e5/sI1Llw4S1nkvP322xwdHBAHEaY05HkG1rWQd3sdLl++zJtv3uR4ckR/0CNdLDg+njCZjJFSsrW1jTWGp08fY01BqGA0PuTM5jaz6YSyLOlEAd1OTJpnTMfHdFZbnjJQevaqqrnKZSUsfAzylk8v3PhJDcPTKkQQQmwDu/75h8CFxvvOA4//MBP8SYYQJ0OJDw1NLBRZWbdbz+dzBNBuxSSJY2kuraW0luHqGlGrTbvdweQpo4M9xuMjDg+PELFCIphNj4mkRGFIk4Ruq81wuMLB0YiDwxFRqwNAmaeAQElBr9sBW5Is5kRhSMf3CGRpSivURKEmDjSRUkjrd1BcT4HWLktSFWgpKQkCx8wchEG9eLXSDZUr6ST3hNfgFNKXWLvnrdeqkFI5ANGrLEmpfNu1u1VdnYczLjUrli+gUsodp7rra70EBEI6khqppE8Pe+Mi8C3YGrAIYzCOHw5RFCg0OnRFYoH0YZcWbG6uUJQZeZ4yPlrQaXUYH094+91bhFHI2kqfa1euMD4akS5Srl6/xnwxZ3d/FyMFVsCFC+dZJBlvvPmOawKLY/CsUGVpSZKUzY0tiiJl5/EDzmyus793gBSBY8XOM0yRs7/7hHiwxXw2pbu26XkwTWORVYVO5qMdAeF6Kz6t8ZMahn8I/IfA3/E//6/G8/+5EOLv40DH8Y+LL3wi41QBgzGnSqEbnkO1YLC512SALMt4vPOYCxfOkeUZnW6X8+cv0orb2NLwu7/z//Hk3l329vdBSLqdPpkp6HdaDDttjg/3ybKEr/3qX2S6SPjdb3+Pdn9IkjuOhMhrOiLgeDQikAqsZWN1FRWFGGtdSrKj6McRkYCWlrTikG4rqqXkqvOohGAC5drBta9BWOptCpdKVZIwjJAehJTeY8AXX+OZhqTnOABQViC8ZidKIe2yik94jKLIC3/d3b6ovNhNUQFuQtbsWUJqMmtBVS3YLnGn/KJRQeR9buMyRUWELQ22LLHZgqjVcRoVylKIgq0zG1gMg16f137wLrN5xtWrV/jhG29zdHjAL//SLzCbHtNutTl79izf/r1vc/W5a6z0h8zzlMOjQ5LFgldf/TLdbotv/avfI9ctBoMBo/GEQX+VxSJhd3eXV268Qit2WZ5+f8CjR4+4fv06pjRk2YL3332L61/sUGYLpzcqJNYKoGRpFPyl4qO8hU93fGSrlxDifwN+F3hBCPFQCPE3cQbhV4QQ7wG/4v8G+CfAbeAW8D8B/+kfyaw/ajxzxT/cMEipsGWJKUpX1CMko9ER3W6XtbV1jGdFdjUNIdPJjDvvv0+/0yYI3E5cmhIlXFdmqxUhBGxtbdLt9njwaIfd/QPy0jCbL5aEJ8YpTJdlgRTQa7dpt2LKPCdPU0IdECtNrAMi7Xor4jAgjAKCUPvd1tS4idaBCwfALyRTN4e53V+i/EKW0nsJQnixFLfwhVJIqZ0X0biBbQWEWcczgFSOlEQIpF/0SwUlC3h6M0ttECpB4CquEN4oCR9+GOEeVkislFjhcA6pAq8OHhNEIe3uAB23QAWoMGRldYWLFy5y6eIFrl65TBS62pNev+c8g71dnuw85dGjx5w/f4HHjx9z5/ZtRzdvnXTfwd4+yXTCpXPbXLlyGWNKHj16SBhFHE8mbG5u8vDhQ+7du8/Fi5fY3d0lSZxupZSCTreNVoJASUxZONFfH7ZVXlF9U9rqFjU84xEIs3x8yhmLj5OV+Gsf8tKf+4D3WuA/+8NO6pMexgNZpyXp3GslQkgsjouhKDJmsxn9fp9et8vu7lNarS5SOLLTPMs4f/YsZjFltpijw4Asc5WSK/0eJstQUnLp0iWOpzMe7zxlOs9QsULqgG6nQ14UTGdTFouMC+fOYBYZgaeon09nruehdExHoRTEYUAUarR07rYLGRTKV2pqodCqqksoUEJjRUnkPaLqpnQ3qs9AWFsv0mr/ErLyGBrpAIv3pJTr/65SBTg3vJK9ryjUK1BXNLsLvaGpOmBP2+2Kwt9NzP3PUoUgCpQjhCmMRUcxUd7BtDOUVgRSMT2e8OjhDttnNnm6u0eSTFlbHTCdjLl37x6/8PU/zT/7p/+U23duc+3aNR48eMDe3j4IiIIALSW7T3bo9/t84aXrlAXcunWHxTzBlpbhyoDBYMh7t94nijSLeUoURfT7XSbHx/R6XTKR0FlVvpR8hrElEu0Md3WelZtQG4fP7vjMNIef3HU+2eMVvtzZrSNXompMQebbrMGSJqnrqCxKhsMhg26Pu7fvIIWi1+3VMf29O3e5eukSP/z+9zk+PnaSctKRkUqPrm+f2abb6fPg4S5vvn0bi6Td6xHEMfNFwnh8TJ7lxLHjCUw9IUiRF4Rauf4BKWlpTSBAC4u0Jda4piklneMtpat9AJdxMcaAz7h4P8C596b0YKD2i9QZkf/xH/62wxak8v0ilS6ExAm0Ok/E+HRuM4UmhFqClY2QBXyK05RUpcBFUVLkjgOxzgyByzz4bIYTzal2WB/mebk+IVzqM4xiVNCi1R3QHa7RGaxhZYCQIa24QxhqXn75OTa3+ly8tM3GxippltLt97j+0ou8c/NdXvniF4nimLfefAtTlEgk2xtbPL53n53799haHfLSC8/x/PPXUFKySBY8fvSQs+e2CELN/QePWdvcdEC1MGRZ4lS+tKQsc6S0DFb6HE/GLn0tTu79wica5EcAj6e1LD/p9fFR43NeEu1GDWw1Cp4qhL4sC8cEXeZVFIgQgixzfIara+uupiGKOB4fk6YZg16Xw4N9wBHJtuOQXruFtICUbGxsMRgM+ef/z7coCsFwvU9pYb5YOO3EoiAIAxeaTGdoJen2e85TsI4jQitFFCgCpdDSLZpq4SjllKgCHaClY3uudmqpFP/Hd2/y7/9bN+rzt8D/8I9+m2vXrvHqq19FSQ1C8M1v/hpvjye8shbQVFGy1vLf/v1/5B0DwdmzZ+udv7qdx8eO2Wpvb4//+r/8TzzDNj4zQZ0aqoDFalQGywoBvjUZIRwvIn5B1NhJNQVvkJRjqrKFRgUxAZBnOSqMCOMYHSgCny5uD4bYsmDvyS5ZlhEEmiJ3MoBbW5vcu3sPawzjwxFntjcZ9HrMJxOSxZytjTWORmOSeUKap0ynU/K1VdY3tpiMJ0hP2ZcuJi7TUpaUZeEEbk2JVoqiyB33pVJ4GdEThuDD8w2nn/10/Ip/IwyDEC5kaIYRwtctLBZz8H0SSkryPKMsDceTiWtY0po8ywm05ujwgCgKfBnvBKkEceRk1DpxzPH4iI21VVZWVkjSjIePDljbWGW4ssb9J4+Zz53GY5U5kEIymcy4fP4cKysrPLh33+201hC32rSikDDwxUJKNoyD814C5UIIhONM/Be39/kLf/7n+OavvczMGH7/jTd44cUXGPaG/JW/8lcbu427TYWAwWDAQyMRvucBIZCl4Jvf/OaJsFh6vKK6oysHQgi4Myrq8EEIwXh8zIMH97n1/m3+i//4r2GFM85CCH7ntZucObvN/v4+X3/luju+dbwFSKc47liVXVZDSInAUBqQQmFK14Ktg7jW/tRhQdxp0+t3SZIpZZEhKVlfXWF0cEQYhsznrmU+SZyo79HhkVOhygsWswWD3oBWHHH/7l2ee/ELnNlc5/7dh8RhiBRwNBqxMlghTwuMhU67BSbDlCVZkmKKEoGgNAVC+NCnyNC6vbyGzZvyR6KPJ3yMn/i+/8OMz41h+Ci58Up2DvCkIKlP34ERgEfsjXWpxjTNOHf2HEmyII5bZFnK4eE+Vy6e4+GD+zx9suPEX3odTJlDkdIKA7a3N1FK869/9/dZW+1y5swZjqczjo5GCOmKj4RQSKXJ8tzvciF5XpIkCa1WRKSdB9IOFLGGUGvvVkveGBm+ekYxuPF1lJSOcajVQgjBn33JYSVSOk/jZ3/2Kw5crRabB8JKj6tIb2jqlKMUHgtogLXVzi787l5lJSrr0AQdPa7Q7/V4+eUvcOPGDW4fLhxg6YHI7e2zWAvraxu8uzOqjsR8PufNt97k1//Sr2Lq1Kfwnoip5+eAUVeHIQNJkLcJgpAgUEgyhCwoyhRlDNub55gcTjk+GhEpzd3b73N0/TorKys8/9xzlEVJr9fn1vu3uXrlIivDVXZ2d3j9D77HSzde4eyZdd58+x0Gq6soHbLIMmSgmUwnCGFcBajUjhC2HbG67SpAF4s5UTSgLAqMKRyYaIWvf3RDfmydy6ozUzZCufJjfvYnH58bw/CjhjGmTktWYURFLqK1ZrFwNQtR5PQMJ1lGt9t1cvRZxubmFqPRGKUU6yur/N5r32U+n7sy2TInUm5X6/c67O3uEcYdHj894sKFixxPZjx+/Jgis4SxKyTq9nrErZjJdEoc9tBByHhyjJCu5kB7QhKsQavAezOK8NqX+JXtbZSUtYsdx7Fz1cVSvKQqIahKvoWtMgF+WEDYJY5orQcGGx6FqH9bptgqmrxT17dSDK89EnHSVa7msvRYmlSoLl3Zbrd59dVXufnokL29XQaDIYF2Ru3111/nL//5ryOregvpWLescOXVxlravQ7Zok2n02F1OGQ2zZFIBv0Bu7v7rKyuUpYlx8fHRFFEHMeEQUiWZew83uHypYvMZgtacUyeZ9y/e5uXXnqeg8MDHj7ZY3NrmzAMKYRgMXchiRYChSXPnbBtkRWUWUGWpLSU46hweA8nLwgnG7E/eohTP//ox78xhkFr7ZSYy9IDicrLws1RStGLewAskoTJZEK/P3SisIXjGtzdecTRwR7JYsHTnceEyhGeSuuq9mxZkC1Krzw1p99vMRqP2d1zWMSlS2eQ2ukOFEXJ8XjG4WjClcs9Ll25wpuv/QC36xq01IRacieRPH/hMs/fuOHqDnx6Ufo6Ael3aiGUe11Jt6tia4+gygw0qe+aICFU7nqVQhQej6lSu1DnLqn2d199IKwvXXbgYZV6kEJ4Y/KsERFNS1MfunnDW9bW1v1vbq6vvPIK7+0cc3R4yD/4P/8B168/j5Lw67/2C+gwwCiFNAFBFNPt9TF5QTrbI1ukDLp93njzdS5fuUSoNJPxiPX1NbAOvF1bW6PVbvPOuzd5/vo1jo+dcHGn0+HwcJdf/DO/wO9+5w+4c/8RvX6PVhRU5pdW1KJIZ4SBpiwyjo/HtFZSyjynHce1tKH7gM8CNQKKj1/t3Exn/An4+GON06nIZs1CJUvXRLwrI1FkBVE7RljvtvrFknv+x06vz9F4xHgyptNpI6yhEwQEWKS0SCWZJynrqwPm0wnDlYHTvNSa3f0RSeZanOezBBlo8ixDac08SRHAc1efYzadkmeZ8wyUQEvBC7/4F/hiGDgX2bv4tVGoagekREhZx/rL9KOo0XD3WeEdA4vwFXVCyGc8iPq2a6Qk61DBNp72KcV6/7fNW1acuIdF4/PN459OT1bGxxrrzUnDu/Cez3A45G/8R3/Di7kU/MHtQ/7xP/nHrK2v8Tf/0i+hohbd/gomK2m3JghKlDRgS44ODsBYxuNjkkWCDgKORkecPXeOtc0NHj54yOOdp5w9u8Xx9BiQTEdT2u0ZL16/xu17d5nPJgjTIgwi3yBlKK3x95UlWyywZYEpCoQpa1zFGQNfyVgb2mc5SutQ4UdWPf506hs+M+nKP+z4wLSOqG5VUe+OVV7fmGWaLdABpvRCpF6AtSitK4Hu9ni6t8vxZMzKcECZpezuPGYxnRCG2jEJ5wUqCEmylK0zWxRF5jgFTYlVisI64tF0kTrBGelKg4eDHlvrazx++JCiyIkijdq8yJd+5deIwgBVFSB5g6CU/1tWVYQVIOgXsFwu5uYu/4EpLr+TuZeWjq27b5epx6b1sCfe1TDClRXy7eoniHeppuPxi+YxG8a7+iw+XKl+t8YuX/cxUpUhabU6/Lt/+d/jz/zCL/KdW/sEcYt2b0Dc6dHptJGiJM/nbK6vMZscI4DR0YjR0QhrLePJMZ1+l5defokgCnnw8DEqiNjaPst0OqPb7vHw/gOkhM2NVawpyJKMKIpdrYe2lMKSFQVCCvIsocwzbJFTZgmYqklMAXKZmfhQ2ib/jhpLaD6gynX+qFTmJ5Xm/NwYBlgugPqnv3eNNUghl6SlpvDAo2txBlcIlKcZ08nE5f2FE3Apiow0XXDx4nkCrfjBH3yf92+9h1aSUEiS+RypNQejEb1+n1dufJF79+6zf3jIPM9QYQj+u42BOI6ZTucs5gsG/RaPHz5gdjzi8bTgype/xp969VWk8Ci+dAtESeHDBnduEl+27HdSpZQLIxD14uGZG8Eut/bqGcsyxKgWnX+hXti2gRE0XqvxgxNhRmUfljdz3SPR8GSqn/Wv1TFq47A8oqifWHoZNfu3XFZVDoer3J0G3M87vH5kiTodjBAskgUXLp7HYkgWCyTSiQynWd089/M///NcuHwRtOB7P/guq+tDolZEKwyJtebh/Qe8+upX2dw8Q5EbbAmTyZTJdEIYB8wTpzZe5Dnz2RRhHajtvNBnLru7nj+yjkHwU3IMPnR8rgzDM8NfeSklQrkb25SGsnCorlKuDbnCIBaLeQ0iZmlCWTitxac7Twh14HLWtqA36CG1YrFIiOMW3W4HhODcxQv0+30mkxllaSiNxVhLaQ29fo8w0qSF4xrQ2jVRpcmcQEm+/rWf5+z2dr3ZO69A1Lvtsrx2uftWK9EZi4aHgDi1YJdNZc37zT1fAYHW247lDt2s0KvX7slL69/27F1czdvWhqT5ycau19gRl/ag4UH482kaNludI6d3RedBnb9wgdn6c9wue+gwpN1u+wKykiiM0MpVJE6nU95//xZpmvLiiy86UZ0s5/Bo5BqrFnNWVlfRypXNnz1zhl634+oidESa5AwHq2xtbjkCHGPIkoT5bILx0nZVMdqPTeT2Y+7wn/T4XBmG0ynL6i8lFVJISmMoyqW3UFUzSimdiG2ycIQh7Taz6RhMhvI3Xe6LkjY2N0nSBK0kxpRO09IFk/T7fYwxpGlGacEK4eXRBVEck5UFs9kcIQyDfof1tXVMXrD+3A2eu3bN4we+ZkAsc//NRVRjBrUHAXUxUb2SBc9sOfUxTlwwv8N7kAxzwhs4dYDl/6vQpXGVT3zqlMdiGh5IYzr+F7t8f/05r8nwgfNoZFM4eR2kkp5zIiSMY66/8AI/PHACvFEU1S32pTUEQUiWZzx8+JDbd25x9swW58+ewZaGO+8/IEsNpSkZHe4yGHY4Pj5gMOiysbHqBGx0hJSKxSJFSEfJp6TT95gcj0kWC4QQrpL0J+JU+HRdhs8N+HhaZWq5yTidB9cCW9YgZHM3MxayosBYS6/XpR075eko0PzLb/0r+t0eg26ffLHg4d077D59Qq/foyx9f0Wesb6+Rq835OBwxCLLSbICG4VIrRFSkeQZiyTBGFASVvo9zmysMTj3HFIItJaEqgIf5YkyY0EDaxCy4Qn5/gZ/ztIDirLaieu1Wf0tnr1GVPjfcpduphWtXYYR1RWWHvisDMsSOzy9yzWMia3/54FPUTfBSm8UrF1+J7Y+6HKujUlU52esM6R1XasEoUpUEKCLgm/80jcYG8vv78zpSziajFlZrGOlYGv7LN///vcoTcmFc+e5fu15Htx9xPh4zLd//3t84+s/z9s33yIp54SdLkVu6fUijMmYzqasba5yuD8i1IpuJ6YVRyhh66IzqRV44NtdJH81fnrJhZ94fO48htpA2KWxqNibKqNQYw1lxTUgSdPUkZt47cEoCknThP29PQKtyTz+UGQZURSRphlZXvgeAscEFQYhh0djitI1FmnPmCSV5Hg6wwjQgaQVt2hFMW/c28Map1uhlXJZB5qeQhN+8iXJYrlQpedWWDoKorETL2P6ptvvFqBf0BUaWN23ta1YAoEftHHVnod330VV8yyWRmZpp713sbQ1zX+xeq6V4XtmwTTKo5ffXc2yCYI2HHYhHaeEcizTQgj++l//db70a79OkiQcjUdMZ1M63Q6z+dw3yEkeP9zh5Zdeot8fkCQJRV6ytbGFLYwvS4cwUvQHHZJsgSkNQRD7R0SaZaRpQhi4npSiKOsS98bV84vuR2UeqvP59MbnyjAsMayTbmgVOrjXqI1Ds6KuKAuUkpRlSbJIMGXJ0ydPiKKQjY0NVw2ZLNBS0u10SdIMgyM/NaUhXSSkWcZskWA89VmgXBt0YUrG0zmFcbwFrdixL33jG79YZ0UCrXGdhyfxgWWCwS4NRBPYq9Z/7do3sQZ/PRrXojIO1cJvJjAqw3oy4Dh98za/u4ETnIqJa8Pi31vv/jWguHy92k0rXOLEpE7Po5mZaICagmX3qFQarUOCMESHAQhB6IuairJgPBoznUwJlKbX6zOfzdlY3+T92/dYXV/n/IVzrKwM2Hv6lG67R7+3wmyS+ExIzNraKtYWTKbHmNKS5yVFXjCfz0kWc8d/CTW4uXR2JMI2zdmPMAwfUcn7Rz3+2BmGD0vFuNx+I81mK+R6WcNgrPFt1sJhA0KSebmyrChc2klJhoMBZZHy4MEdup0O0+mUZL4gWyy4/d67PLx/DyEkKg6ZJQmLJCWQCiU1Om4TtboMBquO+i3Pmc7nCOEWdhRo+p0uve0rtOKW8xikJApDAh0QhSGhDlBSsLzlWZ5T9QCatQd1RWIF9llTewS2QskrNL9hJP2B6gyHbR7fvculDE1lmOzSgHjXv7nRS+EYoIQUHr+w1TecOI3KQtU4ha3etTQMtVcixYnPVx750jhQGxIppf9+z0sZBOgoQIchg+GAL//b/wH7wRor3T7KwkvXX+TwcMQsyWn3h7zxzju8+IUXOXvuDHv7exwejhj21pAiYHR4jFZOTXttfZ3d3V2MKcnzjKLIiaOIUCuOjg4wpUEFmrqd0g/jY6aq6ezDb/Qf8dpPYfyxMwwfNuqKvAYwB8sbsyxdk0vFQ1iFsAqwnmswywtK3/FnTElpStqtFskiodtpEwUBD+7fxRpLq9slKwxJ6pSmF/O506NotQjimE6v50hXspQszwlDFy4EShEozQvXrzeAxKpB6tksRLOd2Z7eZ+rQtTIAjZLlevE1hr82VSzfTAAuswBN8V//UsMYVIbD+oxLvcCrzEgjLGgWmdkqE8Gy/6HGHBCNY/mp4sDb5dQb/6bNNzXvgeYhpAApkVoTxjFSa6f9EYb83M/+LJQFk9ExsQ4Z9PscHh2xdfYs3/2Dmzze2WXrzDmstSxmri8ijgJm0ylJktLp9FgZrhHFHUrj+CYLUxIEGi0lZZ6TZFltGE9ij8L15nzkyv+TUOKPZJwu6ChLl6KsshBVeFGWBUHg+hHCMKLd7iKEIk0zjDHM53MnjtJqIWzJ053HrK6uEsUxpQUhXDw5m838F0viVgsd6Fo81Vrj0HIlCLRGDc8QRxFSOMk7x9TscA8qoIqTRq4BB3LiCe+GVx2Kp1ON1eHsiWOb+nPVMSt9DfAVpJXBoZqObX7E/21qI9IEM5bP+LkZ4wG3pTd32hBUn6vfVnsM1IWA4oMWi39Tw2kARF23IhvEuIHWdDodhsMB4tIrJPMZSTLn7LlzWCF4/oUXWCQLvvWtH2CMZG1thePjQ2azERsbawwGXY6Px7TbHYTQXLv6PIskIcszirJ0dPhYlJIOZyqXStc+iGoEDx8RKvxJKPHJjGdk7uvd0T1nrKl34up5ZfGNOgVKKzrdruNImC2YzWYUeU6r1aLX7RFIxa133iLSgnavS1aWTGdzZos5iyRBhyGb22d59HCHldUNDo/GzBcJhTFEoSKQECpFv93mq6/+HALXCu6MkqOB19qxMi1vJur1dgJ8E03X2gccz2AN9ZWpvYkKhHUcmP4Y1jjRVYeiLl17T88GIIVtLLwGYFnPqbEoreufsGaZsWjOpdmzsTRelSdkPcFLA4xkuawaZ7R8rgrgq7CCqnRcIpVGyQAhNa1Wh3a3x9r6Ou1OF2MMv33ngKd7Tzmejj3gHHHjCze4dfsxv/Vb/5IrVy6zsjLg/r3bSGm4cuUSg0EPYy2Pn+xQloYwDNFaEoSBx2wsUrr0trEghELWgjM/hhfwJ6HEJz+E32WaKUwpPtglj0LXYefwB5gnKYtFSq/fxwL94ZCNrU2E1ty/c4+1lRVKU7JYJEyPJ8xmM0fe2opptdrs7u6jlWYxL7ECWq2QKAhdGKE1rTgkDFztQ1kWIGwt1lKXPstnEfqlGaiRw5PvORVC1Sd46nxP9pMsjy5c6LsEAP0HnkU6TlmF6jrXUclykVceShP4PYFTCjiBadjlTOtF38QWTsEty+dFbZyW2ZzqP9fmrgOntNVqqbNBjgAAIABJREFUtwmiEKEkP/vqzxLFMUdHh0gsh7u7vHLjRV65cZX9/T0eP97h/PmLdFp9Hj18gkWwtrbGYjF3HZgP7tPv9Wi3OkSRI/gV4AyttZ4iz53Ds/v/R6z8T1nt9nNjGE7UMdT+5/L5ylMwfjdza2u5m1a7tBBOldkIQRCEtKIYaQy333mHvb1d4igmSTMmszl5VhDqkDCKsFiCMCRNU44nU8JIEISaMAoJlCIOAgItSYK+CyfEEkk3ttFf31iY1WbY3BGXu/ByAcByl6R+vRGS1Mu7PtSJRVZhHfUUGtjACVvT8DxOXfx6gTcBYXvqeDSOV+EilTGwDTDS1t/TvCz11aiv08lZVF4i9fuqFK8UDlNSWqPDwLE9hQFrm+t8+9ExSikWszk333qHMxurfPXVn+HsuU1u3rzF6HDOxfPPkyWW6fECrQOEtGxtrdLttEjThCx3snRFUWCNdSQ8Ujaip+ZM7amfHzI+ZW3Lz02BU3P3lEJgqm5JawkCpxitlKtQA3/jSUFmS1rtNmmaUiRzCltyPD1mMs/Y3DzL81cu8/u/+Zv8s3/0DygWMw5ywZPRMU/2DljrDog7LSwFSlmGvTZaBOw8fshgZcgiW2DKAq00gWcR/spXvkLlsmsPOGqlCKQEJT0oBy7WZ7mSqTaRhtttLUK6bd42FqWlsTB9cVAzxengAf9+6VeYcPuatWJJWlqvZ/cdwi6brYw1rhcAWJK9ejGapn8hnDivcOKWSw+j8nBsPUXfO7Q0bpVZ83aw3ndF5d74IKvKTJwou6jSmNIZyLJ0zxkhiFotdBiQ7O3ylS9/hec6JY+fHDA6mrJ1OEJaw+b6CirJePutNzh34TIrq6s8efIUpGV7dZ2V9oDd/SP+9Xd+Dx1K4l5It7NOZiRxd0ActRDGUekb40vZrfFqVB9j0VvxqRqHz43HcDKv7UYzVWasPbUTCpDSpSgFJElCqAWBcmm7wcoq5y5cpB2FPHj/XWwyp92KOZzOOJzOKBGO7t0Y0qKg22mj/AJMEkcQCtR0aFpqUtVn0O/XcbgUgkCpJTHq0hLUS6sZp590LkW9o54OIZp1A8uVYptrvT6wqK5TbUAsFvMsaHkyeuBkSHHSi1jG/8sFe/K7OHG805voMnyptEUbc2H5HmeCTrnc1QZxKhRzVaLOq1JeoQsEFy5c4De++57bMJTi5nu3yJKMWIdsbW2yvb3FO+++RRQFrK2tMhmPicOI9ZUVOq0WOtQsspS8LOl0eqA0aV562T9BWeYYz5UpMY6A51mY5tnxJxjDJz8qkA2cFHtpyhNBqcWHFkCW546o2Bi0UsSxyxbMjicMej12d3Z4+vQJURxSWhhNZpTGOCXpQILHBrrdLoskoShylITJZIwtC6eFiaOBP7t9psYa6l4IqEMcs1wDy2VcbZeNxSLqlGDjWVGVRrOM3f1HT4QJiGc2otP3YG2f6jcsF9sS/HR/S7ksLa/e2mR0asKkJ0KTahL+2A37RXPJnKx/WNLELBd+pUtav3LisTw/N2/HkOUUuZ1WiGZ1dRUlJd1uh/v37zObL+j2BkznCZcuX2V9fZ2bN99FCkunG3N4uEeep2xsrnNm6wxYmM8TjDXoQGOBIAxrzZHT11lYTlyXDx6fbijxuTIM1U1rvIqRta5u3VpOsRm5xWywIDSBjmhHbSbHE7RQCFsibYEWcO/eXZIyJ7WGJwf7pFlGr9tBC0ukNe12y7vJmtH4mMPDfYYrPYQsUcr1RURa8+BowVd/7ivEoSYKXZm0EB77F1DUylLixLKo5lxhCI7JyVf41fJyy0xF7Q35hVnhDstjV+1JJ6zQ0nCeMiT1sLaxME9iY0vs8CQuUIOJDbyhGR6dxiDqxWJPLuoTwCKnv3gJVC6xisbnBQ0RWXEirKqaq37ll38ZbMn6+gpJknL7zn0WmeHBk10m8znf+LN/jkhr3nz9NYosYzwb8WDnIatra1w4d4FzZ84hLIynE6IoZjAcEgQBJRYdBCz7RGX9ELY5y8/e+FwZhvomsY0bxO9qTrTVxcPWOtm6PDdewSmkKAqm06nvviz5wssvM5tMeHD/PnlRUgqJlYp2p0NZZISBU57SUpEmKWlaYhFOlFWCI9SwFHlGICWDfp8w0CgpwKevmgvXWFMv2SW4WAFqsn5IIRGeiwEa6k611+D3ygZn+xLQW4YIdVEUy124ubBPe+On9y8PEzaAW3+shrhP45/F4xrLCbnFXPlGVYa/YoBshDDV+VMpbC5v2up9YrnsT3zlid+qhjPhjGvplbqs9zSiMEArl+Lc3RuRZIbj2Zy3b97kyd4TvvilG1y6eIGnOztYA9PpnKPDQ85sbtBtt5HWgY/g2vmTJCFL05rd6bTMzkf7A5+u0fhcGQbrQTVYeg/Vjbssja5uI5cmzLIMiaBIXaGTkII0yxDWcvvWLd555x10GHDpyhXanTZaKbI0odVuebbpHGMEeVGSm5JWp8V8PmexSLHGOJovYThzZhOtFEoKhy/UmYB6y17uvE2foQLpZFWr0EDe5RJLqHffmii2uijLReYKoEyNAVRGtJmePFE+WBkmKtyisXNX88J3W1ahTHNJNkIKGlmfyqg0X/vg4qWGcRO1b/QB73vmTqjnYhvXsFL6rlTAXYGbI+X5rZuPWVkZUBQFk2lBpzfg3qN97j95wqPdJxyNRpw7e47NtQ1M4bgW7t67y9raKme2NhFCUBQFeVHUFtXplpQI+1FhwweMT7nA6XOTlYDKlXU3n2uSsvUuWLVdVw1UWmssrm8izzOEELRabae8ZAyPHj3iwYMH5HnOz3zxS2TpnO98J0VZQ7cds7mxwWh3xCwxRFGX40XCwXjMxpl1Hry5izUWrUNsUSCs5es//zW0dKrYRZFTpe6aO64QHoQUAqGqsxJLV9irUVdUrXW7cmUEhUB7MVrpKGqpd2FrMdaghHL2pHJNKt/dARPLMmR30XySQCzfWhuE5ZyNB9SkaIQt3iBVh6s/S+Xan1z0rnmrnsnSvJwIQ+ypEOSkHavCper36jtKY+ojCylRIiCMLFEUkaeu+e0rX/4Kly6eY/vMBrfeu8dkeoyMIp6Mjnnv3kMCHVIWJf3ekMIY2t0uSM30+Jjnr16j3Woznc8wxhBFIUEUEUZhY6LVdf6YQ5y4Cj/18ZnxGE7U4v8hRjPz0Dh6LY3WbL8u8sJ73I5MQ0chRljCOGJ3f588zwmCkLW1dfI8dxV9ZUkrjrFYFkmGEBqpAhZpilCSbq9DmmZoHWCtS0VaX0dfr7lGUxIs3Xr3+/LRpHOrFkSNJ8hlCc+J959E8Z65FlXZtPGPZXFSo0y5ijfch/3fpp7fsxe9xgEbV7zCBDh5nnAC/KztUv13A8egcsKrlGXlOTVCJrH8WwjpKOat8Lu0z4pQ4SNL8EIIgQ6CevG+9NKLGFPywvWrDAchT58+Zn1rnUUm2D2Y8mjngMk0RQhJWaSMDveIY0UUBggk6+vryMooa1WfzbI3pXG+fAiO8xkanxnD0Bwf1kH5oy7miTx+44Zv/l2FE8ZnLRy3o0F6MUEVBKRZhjEle/t7nDt3jueuv8DhwSF379xjMh7TasX0hwP2j8bM05zSMw/rIGRtcwOEJc1yorBNluQI6zondS0uW2EHbt5V6W9ZljUVe/PmCbwCt/A7OrimnIooVgrhU53GA4RLWZMKZqiIQqod3VhTA7TGlP7h3W8vd2dK/1jGNycNt13OsVm+XIF74tSKr1GIqp/DuEcV3iz/7Rv0dXKJXQhsTUlfYRTGLo9nTdN1aEYhtSVYGlLh1L1qkd/KwzSG569e5Gt/6mfAJly59hztXp/JvOS92w/4znde4/b7D7h44QKDQY+7d28znc0oihItFFcuXWZ7e5tktkAp1/+SZxknLd/Hu58/6VDixzVEn6tQ4vRodgXW8bCP+wpPD48tSfOMJHWAYp4u2Hu6w6UL57h+9SqL6Yj/+zd+g6O9XXQQMhwOGc3mHB4co0VAludMj6fIijsy0GilMKWhKEpKLXn/MF0uIGon3O+qnmxFVru5cBRlUnmg1NbFSEqpWqbOlMap0SNRFfiIMxil8bX5Tbfblx2ZRmghWHJVCGHBeg1JR4vkL+JyITYxiGY4ASfB3oYDRBM7qd5XewDWQAWeejeiMtoVXV5VzVB51tbTtTuFcoMVXtjFT819t/Tp6urfnlPFT3ghXw/qehD3N19/wJe2QjY318mNYVIo1tdXHYY0GbPIMh7t7HL7zgPOnt/mTBiyv79Lv5cTt1qoUIOnl2vHMUWRE0aRk9U7cbU+xviTUOKTHbUlbrq1/kauvIZqhw6CoH7dWksUhuRpwv6THVaHPd67+Q6/9du/zWg0QigX3z/Y2eXRzh7jyQwrIc9ThLRIKzg8OGRtdQ2sJUszQh0uKeARy9r5xhxdetVSZRiklODJRqoMirWuOzTLMuazOZPJhMV8TrpIyPPcLQJfICVoqBhUXpP/vVKzrnZXt2Cbi3upqwHLOokTq8pHGSdSnpw8Tv2XrebQCEvAN1mVfo6+Bbuaj7W1AZXWKYj7JA/Gp3SNNfXuXwGfLpXr8xs+5KjwktOQpcB5I05BOyQINAi4ceMGR4dHRDrg+avP0W3HdDsx589tobUmKw2L3PL+nYfs7o/QKiKKIo7HI5LFgvF4zGw6JfWaIcs0+Y9pFOoL/emNz4zH8IeNuZoIuxAuxjxdflqHGz6S1UqDCdBBwOrqKtPRHo/vvs/k8Ck//P6I119/g4O9ff7Cr/4qr//wdXa+/z0mScrx8dz9u0lLEEK71SFZJBzsHnH10gWSmcUqS3fQphVq4njoC6qq8mC3GzoxeEFRljVqnhtLnmYkSQqIutvQufelWzBSEgQBSkkCrdFKI726VhRFBGFQf05JiVDKeQK2YrJynonzH3CCsY1rB8uFXe3eJ9zfeve22IbOAVTdrEs/pV6SxiyNVY1fmJr3sdr5rcWzMrswpgItkYLSezXSCnwVykmtt2fwkfpMnDH24KrDSgVBGGDL2CmeJxnGWo4nUxbTKdtnz3Lt4jke3LtDmoy4cOkM79+6x8FkxtqZLfaOFjx4tMMXX34J2RUkqaMD7A76zoDP5/RXN8jzHOFYP2hM6jM/Pj8ewxK6BpbAFyytdR0jiyWzkzVQFq4V+ehgn9HBHovjIx4/uEOWzNje3uTc+fMIKTmaTJnMUqxVaB0ShppuL6YdByzmc0xRIq1gOOiiBJRFhsL+/+y9e7BvyVXf9+nu/fy9zvs+5s7MnRlJ5mEZCYeACVV+4ao4lF0kVcHlKlcKB6ooY7AL4qCIR1BMGQwGRwbhiksUSSAmBX6lIMaVCo9QiRNjgonACElII41mRvO4z3PO7/wee+/uXvmju/fe59x7Z0ZiNDMauafO3HP279V7/3avXuu7vuu7eMc73hE8w7EicphksC9a473QtpambVlvtyxXa1brNdtty6ZpaNpQ8+8BIohqrWXbNKy3G9abNevNhs12y2p1xnazoW2boBXQxSxI4kxEbEFE8E4ixpAUouXcteqFWS7iC6NjPsX5F/DK4T1SCbcPPyNAs29a612PlYTWbtEL8g6cxXcdyjq092Bd/LH4LmR9RnBkpEdEbsTw5TPEOAPmEkIzM8IzFKfLU9arFbNJxVsee4QMoSoLDo4OcFrxzAsvoouSsp5w5/YdlCh2d3ZAoGla6qoK4LPz5+pGenfr3M+DxusLTr6sx6CUegT4aeAK4Zt9v4j8qFJqH/g54DHgKeAviMhdFbaOHwW+BlgDf1lEfuuzM/0L42K6Lf0bXXIY+jWmhrVKa7wIy+UpYi2FMWxOT1kc7DJ54jGcVRyfHNM6i85zNEJVlJSFJi8yyszgmrDw8izn6U98krc89hAf/MhHiX2kuXR0acgwhEmF3+PuJQSVamttjys4H1Oro07RKio6iQhuhJ0oHCgwXdcL0hijo76DIcszqrLCRLQ8rRGlNNqAeIUoF+cXu0zHS9rfnhHr8KT4f6BgK6SHC8KxYXcUOb8wRGQo0koRSpyQSqGVDcAoAhJbCbpIWdfanJuYdR6JgrsqVjQmvEWSl3Ax4ukjo+CJmPi+AqAV1js22w2lznn4ylWWyyXLbct8Z8Hp2Za7Z0tEKYqyYrXa4NoXufbwNebTGZsuXMeyKKK3NUi/9t9+b6xeu+X/6foprySUsMDfEJHfUkrNgX+jlPol4C8DvyIiP6iUejfwbuC/Av4j4G3x5yuA/y7++6qMPj99AUPoS46JoGM87r1gjIqCr75H0L33iA3kk7bbcOfODdrNKcvVGV4M15/4YkxZIp3nf//Ff8HNmze5dHCAMwVuuwm7geRoU7Da3uXt165yfOcmH/7gB7l09WH2F3OctWgvBNhAR/dezqVNlc6wXUvXBeZkFqnS2sR2et5TFvm5c/XOR4AxGA2fshU4Wucjs8+R6fh+AnVZMpnUfdeqLMvAmBCLQ79LG9H0iVGlUVk2cCtU3Ok9oHzvqfc3vY9xvyQEQlBG9bL9CgXe45wPIY4IMsrGhLJlj43GLV0j0veow9zFS6xBUBiTceYcVV1TTuphY0hdqlC0KVQzeshQxbPMTI7PXaiudZbV8g54z2p5hjhLmRVM6ppbyxN2d6cojrh5p2Dbdjz/yaf40i96G6vlKR/96Ef44re/g535Hnu7Byg8Kjc4NMqnizRqBRit44MKKM+HZOfHS4Xdr1Ya9GUNg4g8Dzwff18qpT4EXAO+FviT8Wk/BfwawTB8LfDTEs7s15VSu0qpq/F9XpXxIIS3J8qI6jGHnv2GSmhQ/zwAazvyTIOEXXfbtGxbz3z3ECewWt/haHeX559+Ct9uef7Wi5QGbOe4eu0RTpcNO/uHZNpE2bc9Nus1uTEo7/He9ow9L2EnHwhNsW4jgWhxl1cxLZdnQSOyiCCpF8HZ4PZbF+jN3nlUVE7z4ulaF0MNwSmFNRrlPbYLHomOuEQA3XKKskTF9nbBcwgeRHApBPHR79GgJdC8hRgVXUT7RyBvnyWQ0As0AaM4jxbBtxbbtjhr6boWZzuctdjoIWhtQIMxecwuMerwHaKMgH10OO+CUcsCpdlJwCdSFiePx5xzPf+j9yzUIB6rXEjGdM7hnGe5POPg4IDd3T0+8uxTLFdr9ub7nJyuePbZZ9ksz3jm6WdZzCfs7e1wcrYk85pHyzqoOHUt3uRkZOEeEI2oUWr21VoQn4V3/LTAR6XUY8CXAv8auJwWu4g8r5S6FJ92DXhm9LJn47FXzTAo9SDTkJ4w/JtSc0NxUdoNI3/AO8QCXtDGIFqD1pRFSdd1zOuaS/u7ZN5SKc80i2UwRmGyHCtCNZ3yzLPPsphUHF064lPP34jZD2FS1+gspZ6EzBQjiCHtppbMhNx6+FEowq5eZMW57Ik1Fuc8OQEbsNYFAVvvsRass9ET0VjvcE5QInTO0cVS7yLLqDobDI4PCwit0FkGYlDZQBrqpfe96lGCIEIihO4uo8xFTJWmuQoBw9AEnME5h+86us2GtmmCIbAd221D1zahH6dOJKEscAuyaNyVIldBQEcBOtd92IVA17Vs1iuUDhWNPQlMaarpJHy3hC7fJFBaRyBSpRBFUxYVy7sn7MwXHN+5w87uDpcvH3H43CG3bt6i2WzJsoyuc0wmM9ZbS1l6to2lchbbWc62Ww5QZFmOjV5BwkFEVL9Zvdo45KctH/cS4xUbBqXUDPinwLeJyOlLuCz3e+CeS6CU+ibgmwAefvjhVzqN8GYy4Hfnjw8fcz7cGAhOQH/jNLajsQ223VKWOc1GyMuKt33BFyFdx1Mf+TDd3bv85v/5K9R05BmUe1OaVrA6p203qMzwzHPP0tw95au+4o9gdIZ4WCxm3HjxjNl0l0yb6O6nPhSOJG6igarMoST2OtShVR1Bhl0ztL1HBTHZVIAUsIgAnLrYk7NtLdsmiNE0EVRMIKeLKc3Oe1rnyE1L23Uhds8MRVVS5DmSdltjAjgbPRgT6Qbh+qcswijfHj0FHwlLGsA7bNNiu5a22WCbls1qRbPdhvAuhQsKwIf3B2wbsgRZ4VAmFD9hNHnkHphY1pzqSPBCZy2u2wY6ujHgobUdq+2axe6CLC8QCSFbvAnxSkEsntLG8i+fPOYtpSUvCk6XxyyXS/YvHbEz32Fez1menPHMp55D64yN81x+7AnEw3K15rCuKWc75JMpSueIC02H8C6GWKlcbDAOD7INfTbm01kXvFpm4RUaBqVUTjAKPyMi/ywefjGFCEqpq8CNePxZ4JHRyx8Gnrv4niLyfuD9AO985zs+Ldv5SuOoccgA9LH9oBDt8CK0bQsomm3Der1hOpty4/kXuPvCCyyf/xQTA7PFLtvtlnXrudWegXgsnqbtaNqGw8s7eC/BPe5aqrLEi+Xjp44/GpPaqYBrvLBMFlBxFMEopLy8UnGHGQmkJrGRCBwqHVJ7XkJXLWeEPPPkWdfv3M47rHchoxG9JPEOj2C9xxNi98zouJLP4zVK6+i3K7xPqeBIGoJwJwbV01E6EkSCwfKdpd1uaDZrms0aa23o7Zmo2YDJTAi1VFjgLmZfREKn7yzLA88q9owITWVM6BuRRUapBhNTukPWA2gVq/U6GCI0JuI1fSWFiqpTqRdFZjBZAcpgckVRFjjnWZ2uUCjmkwkZwvHtm2ilefL3P8GkKti/ukuWl0wWC2bzXapqgtIGUlirBJGUO4GXXcKfEcHpNQwlYpbhJ4EPich/O3roF4CvB34w/vvzo+PfqpT6WQLoePJq4gvwAI/hnoPhwibkP4CQQwot9Zcoq4qzk+PwfKXpfMitT8qSw90Fn/rd3+Lxhx4iyzRd13F8uqHddpxaD0bROUeRGS4dHlEUFWerFYLgfIdCuHr1SkyFCVqZHpRLc9Q6i6BY4hio/nR8QumTJqSP3AelAlYSQyIN8W+BiFOkqsHOOTrbJVU1Eh05ZBgcHeCUw2lFXoSYvDNd2H0UeBcMkNIBg/DRc1Hxeif0f3zZ0zW2XYfvWppoGNpmOwjoZAYnQp6FVGEX9RKTkcnzHJPnTKZT8iL0AC2KInBP4memhSxxEZki1D145wIZyguF0tjIKHXOkVGeX3QxwZIyPodHR8jqObxWVPWEtu1Qm4bpZMrJnbtI0/DwpUPcZkVVTXn645/iYG+XK48cxV4WhqKoMSrDecFJG0vth2xECslearyUx/DgeqLXNpT4KuA/A/6tUuoD8dh3EQzCP1JKfSPwNPB18bF/QUhVfoyQrvzPX5WZjsb9YqmU9jpXqSiMDINDxPQXtYwty5w4yqLiaG+XzeqYK1eusDOdsHz+OeZ1yRMPXWWnNDjv0RPNw0fXmMwmPHPzFjfWDXlVsN5u2ZnNyIzm7smS9WZDWRQc7e9z5epD9Cy9WHIddtuwUnUsA+7j4rTYkL61el9IpAatRY3GEanCOuET9KAr3gCewju8z7BVARLbptkAeHo3klvzgrMWl2XYVDAWu1llCsRF0C4aBolZhXTzJkMSN0cUiqZrabeBTzGoGwnaezrrMAryIsOLpzQlWR7k1qqqpqonwVuJXkIAEk1cwKoPB1QUr4FgbLwIog15EefrPCavwnlL4H6YLMeY8FyjwYlEzMhw6fJl1I0lZ5stRVmxXK2pp3MmVY2dbPHtlicevcq8NixXW77inf8ejz/2BFeuX6OrSlo1IctKrBOyItS5OHHn9n4BRHOxSdWrsC5evfFKshL/8iU+86vv83wBvuUPOK+XnlP4oHuOj1l5o/lgjOn7CEJwXRNFGA1FXqBQTKdTbio4W69ouy3tesnOtOZTH/soN+8cozE8/pa38tClI1bNBguovORUKwqjsLaNN19QeSrrkoOD/QCWjRq/IiOlpTjH1KmaPv4UvLNxd3eIVihM9OrDSXonaKNj7BqujFIKow1OXMQLssi+I+zIWqOKCPJ5YvFW6AiOhL9NbDHnnQsp18QW1TqAZ5I6XUvMTiS8QSJvIBiZRGFGg8rC53nvEBU6QRdlSZ4XaB3k3VO2IMuL8LeOnb3VINCSSGlKD9+1EjA64CGhwCwRlhRKZRgfwEvlHMSqSxKsEUOKoJAVUpyz+RzXWfLYNGi1WiHWslqecnrnFi90G65de4ijy5f541/5x3ns+hPYTLFSikbKEF15h1IlnbMDjbDPSLz8Ek6VruOweWClPuD1r6JleMNQov+go7+ACQcbOeypvDjE14PVMMagjMaYDBHI85yyqsB7mrbl9OQO/vg2J3eOWa+24DXPPP0MT8zfyvVrV2jaZ/HKYCY17WaFjZWJwT4J8+lkSEWeixkH2Km/Sc8Rs6RnBOIEo0PK0Hto247QpNvSdTbWCPTrh3pSowiiIdZ2UdpuqItIxUOhB0IUKklXLIKGQjgPpxzGuxhOaEwkOdE3zZLBa5D0HiFDEgDWAKj6PI+UbE0mmjILfUPzvIhek+mNQjiexTDGoTMTy8nTQonfafSwTMRAwulFjEZrROnosgvKGDKl8Fr3HuPg4tCHZCjF0dER0t1mHQHT3OTgHdvNGV2zDQav69jf3+fgoWvsHuwymU5YdQ250mRZgfaWbhuAaTHRQKXvWwg08pfzFCIge644rb/ZX+a1r8J40xiG+2EMqbbfxQWktQ6eg/OUZRmKpqxlu23IjY59CWdMJzXLZ59CK81ms8U7YTbZQcSwWbc8/fTTfOEffht/6PojnJytuXN6xmlrcT4Aa1We025WHD56DR17DMSgMU22nyF+yLmHTsiRIoyP/SEVm8ZGkNOy2nY4CalCF8OMTJtIaDLsHRiMgdX6jNlkinKezWqFtxajNXVVMpuFWLxzXWzZHjAYJZEZ6QUnAUl3nQ2FYJJBloBFgZgp6QsxleqD6GSIjMkxWY4ykLkCrSDPcjQqZFK6gGVkOng1XiQwMZ0PLewjDwEFKsvAZL3hIeEYEthKonJJAAAgAElEQVSrIiF0QjSJmBkTQSFtGTUyQ3q4v/oAaGXQOkNhqasaU+bYlaNZLbE6g90dmvWKzGj29vd54qEv4ZG3PMbVx6/TesfN2zeppxWlNhjp8M0ZtiyBClQePksiFkQknaBfMisxxkHGnuVrpePwpjEM4R4YsIcxeDNmvIXDwXMQpXqvAa2Csm+TM5nM+qYhmda4tmPVWrzKWOxMUSI8+bFPcHh0mdl0xrrpWHarQLNuGnZmU9rViiLPyMqin12aaUL2w80di6pERUMQ/GEBnEBnhZPlmm1rsU5onEMl9eEIVorSiA64xcl6A0qCyEwZGH6TxS650ty9fZs7d0+wXcd8Pg3dsLwPiyelGCSqVQs4HEZrnHWo/HxZTeII+D41kcC8ESjog/dR6IreS/IMHoUJqd2mc6zW29j/0SEIeZFT15MghSdQ1hVlWYVOXUoFIhiKzge2pIeQBcAjPnJNlEola0MGJQK6wz6eisBiGjQaJCWhbuOF559jNptFvFfjrGX/4JBc54jSTKZTlHUYHMZZyjynac9wbYXYBZ0IRV6HTA5yng593sk9f31fAnh8LYzD55xheKkL9iB+Q5ZlIQXmgyiaMRovHhd7SmgTkOs8L2maDtsJi8UetyTk05VA11oslizfYTZdcOPuLSazhsXejGoywS2XYcd0lklZ4bfb0HAkNqtN+0WgFp6beQg/4jkorVAmw1vHarvl7umK9aaLxVPhRhfxoBXv/dH38a7v+PYYFmQggf4b/AjDd3zv9wHwt77ru3jo6Iid/QOWx8fcPVlinWM+nwTWpwqybD4yCDVD6bP3Gu805JHSFIlDETgJGINONR8qZkwUWhnwQV4/hSkpvAmhtmZ5tuZkecZ6s2W12YZGsEJfbZrnOQrBZBlVVVLlRfB46pqd+Sz0+8xNYDq6sBNrrSF1C4jgZZ9CRXoKePhO4veBROXtINwymUwQCVjHer1FJHQZ2zRNMJJOU5gKu3Vcfush7ekSe3bC6vgu1eElyskObbvCN2eYyR5aIi9FXOSEvJLxKqKSn8H4nDMMDxrnyE0XABtru57ToGPRTGDGgWCp5zXb1TaQdbzmbLnlne/4o5w9/SSnH/oguFDhZ4qCejbBqIIqn1HUM5bbLZvonp7cPWZeTZiVJfXeHta2TLKUCblPKinxLCR5ChqPpms9t+6ccrre0LgIGEa2oY6ofFGWvOs7/otAk5Z0npamafjx9/19/ub3fR9/+/t/gNOTY7r1muUnn6LMMg72d6lnE06Pj9nePuZgb5ciz7Hien6DiQVcaI3XBq/joiPSkk1gCSavIVz6sDeL0WgdqdpKEAfe2agMBYrQSfzmjZvcWZ6y7QK/wilFXNtkRYUAZ22H1opcQbvpOFk1GL2kMJoMocwz9nd32N1dMJlO0bkOFa4GnKhQC6NTc6GY1YlUch09ByTtwoFHUhQF89198uoFHJq8qinqCUVVcPbckkkxYVLUXN47ojrY4dlPfIwbT32c1XOf4vSZpzncPeDyE2/l0lf9CZpViSkmKD3rvZZUNpE0Mx40EqA7vrfvizd8lsabp+z6PiNdvjbKtSl1vk6hyAuUDjRYkcCcK8oqUI7LOsSdWqOiRoJ4S1WXANRlzd3jE+6enATykPeURSi4MVqxmM/4tY/diESlFM8m4GtAxiVlFyXsyNZ5NtuWprN4NJgsgGZR0agoCmazGTuLHfKYTSmK4GYrYxAU3/rX/nrAS2YzsqJAZYb53i7kGcfLJSrP2D3YQ2d5dOGDlLoniuZGENX5sQ5kuKKJgCUq5OIThpdgBtSQ0kyGL3jlErMewq1btzlZngXtCRFa7/m+H34vP/B3/x4qywOWoAxZXlKUNZiM9/zAD/KeH/o7tF443TacbLacrDfcPj7h6Wef44UXbtBtm5gV8dHQ0lOmEyaRvvvxTZKYlyG0cnitmcwXwYNBsVptyPKQ4lzsLJjUNYin3Ww4uXubZrVEmoa9skTOTrn5yScxdov2rm9cLBDb9an+2r3y+/i1wRXG401jGMYiI4Pr2D9KUEgKkWXQDwjyXuI1eVZS5iU6N3gjbG2LRVBFgcmzUNXnIVOGalKR5Yq6yNDOkXkhU4oiM8zmM0xuwo5e5BweHBJydTqBzMMCklGngZTi876P+fO8oChzstzwY+/7cbSCIs9YzGcc7O4yn88C3VbroEhc1ZRl1S+C7WZL2wVNSkHx8KOP8tDD10BpzlYrsjxnd28P6y3bdptkT3oyWDIKgR2aJBr7rbbfgUmRUTIG8XqnmL1Pv0bjsVwuWZ6dgdZYF3gS9XTCd7/7vwQgKws669m2LRjD7uEhO3v7AHzPu98F2tBYRyuw9XB3teXm8ZJnnnuRWzdv0W234KKOpQwaE6ncOxQzpWyQjDJCqu89IZmhmta4dktuFM5aZvWUxWLOYjFDV4ZVu6ZpLVU5YzLbJctKdue75EqB7bCbDa6zEVy2iBIEDRLEagezev8x9hLkJX2Lz854g4QS6g/sHulxKmr8zkpRFGWflRjEQ6IamtcYDFVZY/WEjd2QlzU200wO9ihms9CdOmIT03nN2fEJeQaXqh22znPabDAKJrMpVZXTrtYIFZcuXQlz0ONYG3oORrQWXddFSm7wJPLMMJ2UqE5jN5sIDL6P73nXt1MZQ50X5FXN7u4u6xdewCPUZUFd1SxmC8R7irLk9GzJdtsw35lz9eo1mvWKRT3h93/vd3lxvebS4T4HB3ssT09YrS11WRGozx48dGIxSmOzKAqrQto0EKry2IQ3ZAkCkBe9MQlhmu+6gBdohVhh22xZLk+oJjWnqzU7ezvsH13CacXvP/kJ/uv3fDc6y1hvlxEc1hxevozWive970d5qvk4b3vbF5DnOb/927+N0T5mc8Ap4amnn+Hs9ITLVy4xPTiIuI0fuuFJ0pqQviZDRazER6+prGtm2Q7b1ZLKKCa5wbdbivmMK1eucrC/h5nXTPb32d2/hKJjWk/Ymor2qSfJzBnldEq7OiOTAAx7ZYNBlQztY3UvbQQik0jwvePi/fxaZSTgTeQxwIMv3EVPIuENQQCkQwshXy06pK4kEH129w9ZHB4gucFkmjzqGFjx1LNpXMhChqE0JbOqZncRmpZoHTUP7plbKgOnd7UTsw8FXoL76ZzFALOy4Afe8538wPe+mzIzdJs16+USbztmdU1VlNimw1lHnoU+jPsH+5jMsFyeogw8/PC10DUrM9R1zdXLV3jPj/wod27fZlpP2N3Zxegw15RadX4QSPHe9q42hLBAaxUFa0Ml6JCAHTgBxJoG56UvZc5MAILf9f0/zOVLV9jd3SPTBTrLyYuCejIJ10EFQ/LCCy9yfLLkdLnGWmE6n3Pt+nWuXrtCWeUgnh9+798LIrJas2oajk9O8a2LEnBJePaBd03IpMQ551nBpJqQm4wsz1DG0DnHZDplb/8AdI4Tw3Rnn3o+o5hMmR8cUO7Mub06Zd1uA5DpFfNyQqXz3ggljMGrVxBIPJD2/NqMN4jH8GmMB1yw0Pr9QS8ZCYiOQw4C69Eog9YlzYmlKmuca3Fopvv7PP7Ff4Tf+79/k2KSM51VtE1LXpYs9va5+eItvPNUWU6pDb7tsI2lriryouCHfuJneP8Pfi9JF1lI6sxJGZlett17H/gJQuQbFJFbEObvRPAEA5IrCenQuubhS5e4efcum+Upm9NTyrIEJaxWZ+wtZjz++ONcvnyJ9ekJ2/UK1zZ0XcuPfO+7sXrL3Tt3mc2mTOo6NtZ1cUcjqml3QMkg0UY0aCks0njcCIMIxVjeC8pAUU8wriNrc1zn2GyDYvZP/9gPMz+YolDUVcliNscrhclzHr5+nbOzM4wOuhEnp6ecnp2xd3SJnb19MmN459vfzt3bt/jwhz7Mu77tr7F2Kya7cxb7+1STCdu2xWtNVuQ9LT7MNwV0DGFE/DdlJoq8ZjKZs7OzS16UzBYL9g/2KMqa6XTBwf5VZju7iAnnruuKyeEe04Nd2m6DNgbvhPXZisYsqao6flhKl0jfAfu1YDF+JuNzzmOQB/y8XHrn4hegtcbooOrjY/pPPJRlRV2VtNbRYTh69DFMXaMyRVnlCJ48z+i8x3ZBAarZNpzcPWZ1eka72TKZTHvjkz5n0KC89xsfGqaE+WQm8CcyHfbi3ChKo6nLgqrIyU2gYH/Dt/x1plXB4WLOoiopjQbboZzl+//23+F7vuc9HCxm2M2a9ekx3XZDs92gtWIyqZlMJ7GkOaR0x41/x15WAEd9b8QSWhJwSUUCG3p2QBQ/0VmGznPyoiQvCrKi4Ju/9/uZTWcYpWjbLeIsdVkwqcuwt4uQZTmHh0dcvnyJIi+igIvn0ceuM5vNkK6j1JpFXfHQpSMuHR6wv78Xiq1MRjmZoPM8GNMx4DgCnoc/k6EYzv2L//w38Cf+yn+DqCC6O5vNyPKcsqxZLHaZzOZY51httrTO04ngtMKUBQ7BicfkBaApsmLUoi5qXr6cnghwrmvw6zA+9zyGz3CMb/bwdzjWdQ5nhTIvMCZHKaG1FlManNLsPfQw08MDjl98kbLM0EpCDwmXeBOKzWrDenlGWRd455ktpmit+ZHv+jbUXlpg9OQaEvA1mhuJOxDvWB+FUVXMsaOj4nMsty6M5h++7+/imy3f/G0BuPvv//578V4oioKfef+PA9+K327QRrOYVLRti8vD7852tG2DbdtAKZZQ3KR9zCao4dZN+go9OajvmZmScBFCSeFGknOPz/cuFjOZnP/1f/gH5GdnNE1D1gTZ9tIoFtMpbduxWa9BDPVkgjjP+uyMZrNhb2fB9UceoTKGrXjsZo3YlsODPQ6ODgKrtW0oM4PJsqHYi2FTEFIYGU9mfH/094jmk7/6c9x+/jnkd/4NWZazs1hw6fCILK8wJiMpb3ln8QidbfBdR9u1dN5CnqHKEpVlmLxAepMpvaRb0KN8qRv207q9X/XxpjEM4zTkxeOJLTbWYwjFVYHTsN1sKIuS6WSK7TZY8ezt7+PalsnRHkePP86Ln3iSyWyCay3WdxSm4uT4mO2mY7laB+ouYUFkZUFZlti6Cmy5LsbojFh45+YtfWwvMSA2KlT7JQVmIvindYD4XLulKHKUF/7hj/0QRVliqkDt1TGt+Ys//ROIWuNI6TGhyDLyPMP5jLouWC3PaJs26Bio1Pw30XYlNr+J/T7zPHba1skjjl5DRP49qNjlOzE7g0cGpsiZzqeY+PhkWoUMgQK32XA4n/L13/hX+B9/4h9wsjxDK0F5y3d+57sB+LVf+SWUazm5fRPlLK5ZU1cF0/kUB+RliRGipqQLAjDQG4G+wK43eBdWntK9cchyw2QWUsBiHYUuqLKA0azWSxSavcNDZtOarMoR17LV8JxSTBZz9q9dJd/ZRc9mWB3CRiVBC9PjkdiYdOx/3Tv+HcZwz3gpNPalmI8XR3qu0aGuQEcV4cSq896yXYcctVdEcVjNdDanbSzWelqluPLYdf5tEZ7jugaTTbjxwg3WqxWdhaZpaPFUXYvE8ERHbUcTOQiBiRfnfq6pyyAmM8CTgbSHMrGwKVZbeh84EErIdIZ3NvaU0BgELZ7AjQy7Zyg60umjwrwUbDab3pUOsXUsPY4chMSxSJNJ8fcQXgxz770CAVTqGBV7hZpg2FRUpCLLyfKCqnI46+iaLqgcGYMRxf/2j/9n3K5wtL8TVKGzjF/+xf+FLMtx3Rmnx8fYtsMA07qkrCokU4N3MirRVtb1WYcUDo3vnWTQ+otDSmEHj8gjrDcbDnf3EO959hNPcenqFaq65u6t5ykLTTWZ0bSayXTCdGePu3dPMOtVzGrlFHkRcJNITkvNfAMYmczTgwzAgzGz12K8IQ3Dqz3CzjEsvuEmSZkDRdd1cVc0ZEXGdtPQNB3P37jF4bWHObhymbIuw6utw24bbBdEWTtrsXjazmNMhslCE5sufVbcpi7mo1Mbud4kpIUmg06C6hVW6EON5P2EBavIdRZeC726EkjfL0Fi8x3vhc1mE16b6b65rlYB12j7XhMBJNPahN6ZvRbjYDDuvWd79+FcLI9KbMPwmECUvoeqrIKBsBa/WWNi5kQraMUinaVzHT7LcF2HFse0LoPiVGZCbYQKdG4RxbjhbWZUrKpNMf1ozmO8R8Z7c0AoRQtFXbG7u8e0rsFabr/4At5ume4saBrLssrpmg31Yh/fdWSdYzZf0NqGrcCinuIzg8pNDxd4QEvUjhjdm2/E8XlhGHr58nNpy7CAyiyniWm5wJH3dGLxjSXLCj753HO84w99Idfe8gXceupj5L6jMIb18ozNek0r0DpHJ57NZkPXdmgdeBGbdPvpANAFEZXYlyEu/lDgNVpm53CI4O30BWASfQchGCOrsNrhcukViLJC94pPXquRgnTYUfOi6hWeOtfEfpnhg1N5czKiRmvyLKcoyr65Lkr18+hLiVM871MqNs5VHIn3KZFslBU5bavZtCukc1RFSVXkoAPnQGzAVupoXJP4LJmhrkqyrOg9FBN5FDrK2wlJ3FWhTJSxl/PyfvEG4LxpGzwHSeemFIv5Arda0zVbVLuhWx4jhaY0hnZ1jEGYznf73h+7+/t0mcbnFXqyiy5rKHKsCnPT8WO1BMVtGCjSF8frDDF87mUlPpMx1mDoj8Vd2Ed3O0m9ddbirKeqKqqqJs8K5nsHHF57mK1zTOoacZ62aWi7LvRCkAAv2abj7HSF74IbnBV59NcHzQQ4/6Vr6AVg06Mqxrs6ZjOM1mTGhPc0GcZkQafAhPZzxDhWVNolR8Cm1ihtMHnIy2dZ1jMaRQQT34foZfSheAofCAK0RmcXwrhgvJL+wbkshkSj4aMhDG8YgUtNPZsxWywQBevNmk2zDrF9FsqsvXW4rsNb17MVk46EFx+KphBcdM8TxTxKrsTUte4/L13HlJocfwsX74xgG6O6ldJ0TYNtGkoN0m7ZLk+wmzWb5Sm23dA228BWzQydd1SzBflkQacNOi9G3gkDJZqXAR7fAOPzwmO4n/kdyrBVvBk8jbVstkEe3OQZHmE+n2OKmkuPPk5WTtidz3j+9Ba27cLNmcRltcF1ltXpGbZtI2GoD+SDV9DfxOfvitSiDtxgPJLrjupFYrXWg76iIhg0bQYdRIikq7CwgxJTlEPLQrv31APTOcu5SKZHEwejYLQJhiMpIqXuU4SQJU2kR/S16j0bFY2KiuetlcYrF2jQQF7WTOeOZrOii0rOefQSkpFxzqLEh65cxuAJEmxaRTm3eG1tqoNB93G5xJMLSYBBYWp4wug7kNEvQuj/IIrMZKFhT9citsWJxjaGqqywzZZ2s2azPgsSbrmmbS2LvR2K2RyrB6l7JT6EOwziMTo17HkJ6PH19Bo+LwxDlmVDb8bRjxfBqCDv3XQd23aLKGJbt6AmvDvf5fj0jKNHrrNzcIiRhs16Rdu2ZJmhaSydtWEf6ITNyRnNaouSoAgVRESI/PyQrhIXQxlUr4ikFOd4BAkI1PHHpB1vVKsQ1KRDO3gT+zAE3kQCHHUsM9b0smsxDZpFgVrvPCqClsT+FED0UjLyrIC0G6dF34cS9Iut5wlEY5u6V8WJRFwjD8rRoVsMk/kO9WRK1zXRUEmUuA/uNj50muoxC6L6iopSdLE4SRvdhyw+ZUiike3JZNAbg4QzxYPnDoSzynBWkWnD/mKGrD1qE0PCzpLpUCHaNg0THJ1tWC43TOoJi71D1GRGttilcZ4yD9JySJhRon24133pv/T4vAgl9H1c3d44+EGd2ZiMsqoCRlBPmM5mTCY1TWuZLnbYOTqirivEW7y3vXsqXnC2o2lauraJ1Zyeb/j27x5k1BLar3R/U/aebY/yR02AGMtHD7z/nGQ8TKRmF0Vo4Z5lWeguVeTk+WAgUpdqCIxG520gHER8Q6kQZknUfeyPJ08nXDFSEVL6G0K3L84dG4Ug6fzif6GeIjwn9G/IgoclgjJZKACraoqqoppMKKpEiMpRRkfwM4RSxsTzi9czSUMMQ6KHklB9Oe8d9DNlbB1G56Aw0eDqvGA626EqS5T3VLlhUheI7XCx5YC1oXFP17VUsxn5YgdrSopqAirIx2uRfmNI40HYwoUZvm7jDekxnE/TqAuPvcJL1gN2F0RXR0bCGI0WwUbJ8ros2TYtVoJMejGpWJ2eUFU1vlXM9/bRm9vMpzXTuuZuY+Otr/EE8da2bTk7W3F6thzmGzEITyhO0mroL6AIqL3OTJ99QIXnJ4OVduMBWwgknoFdaWIcfb6sPFGb8R6JLnwyCN5J3ycy0IWDgQtdmeLzIu8jvC6FMOn6Bxk1pYhcCx9DiCH3cs6biOdhjEYRBGEl9vVQSkemIKSFnEhBEo2pglB7EVOTQ+YjzC4YLh/SvMRMjAyP9RmgsYOQihhGS9TakP3ouobZ/gET71mtVmgP4lpEFNoWmNidvMwrpAvVnMVij3ayg0wWeOvJTBHPKHqKyg3XscdE3pjjDWkYXhV7GXdckaG8OWUmhgUVFq1zwWvIlaZxoedjay2gaNsWnZdoMcwPDrn15AdY7O4wP12x8msEodEt1gaeRNO0PPvMM1SzOn6GQZJ4k4surtI9Sp9ic600Ht9XgabroCNWgFa9JkASS0kIfCJHDaYmnbCKHsKwWyX5+mQ0vAvn712kPMtglHRUWk4gfjJoQ07+gueQjMLIEGoZAYFxFw9zMOSxI7TEUnOdwhGGxZvAwHBQ9++holuuzhkqf+HeSaZF0vTCNTj3jFTWHI52bUO7XSFiqXb2MdZjZjtMJnOcrLGbFTNVMS+nbPMCL6EZr9IGplO25QxVThEXCvO8d4QGwB6UR/m05AyMPvfieL0DjTeoYfiDjUQaepCqUxoXu1QNu630x7LIuTfGsHdwyMfOVuzv7jI7PaPYdgiOqshxXrDOY7uOzckZq5Nl+uCA2iNo0WER9AQnSCSkXlCEmJfv3Xc1LHqh94QG7C8tpIvnF2664WlqOBqBxhTvp5+wWfduS3jXCEz2Hsw4gkju8NgzH815uPikmCgs/ih359Nu34dNw+f1pxZLuBl7Dmo444HZqvslnuagVLhOQ0Ocez2EdBKJBNV2HdZ16Eyhs4BR6DKHLMP4DIVQmIJqOkNNp7RYlNuiNZisREyOjirYvQ5Esmv9Z79xPYU03nQYQ1hA9+K9F0uvgX4xAEPKLe5cmdYUJmNST8jz0P9gd/+I26crKDL2Lh2wWEwpq4zZtGI6DcpNOEE7BY2PtUUqLIAE/492xDixyCVIWYGR0tD4DMIqGYX1g8zbcOelVRsWgB/hBknDMXEVvPOj3g8+chAiwzLewCGfP2QAUn5fp47RyZD0cxq8i/PoQzr1GBJlWWRhDjqL6VjvJlz8XtX51GhyLuJVPfddD/uw6j3HdOHOhTkX0QkRurZDoclQKGsR36J17HcqikkxITMl1WTGdD5DZZptt8VKaGyTFQV5Xg4s13ND3/OZDxqvt+n4nDMMF8HD8Q+cd85k9Pzkzo6f66NK0bi9epHnGDMoNpdlKKE2ecF0d5+NaJ56/nnyOuf6ow9x+XCPw70FRwd7zCYT6izHblpuPX8z5OD1sLASGJi6J6WMQwp3ekMQd2ijQ7u5zJiex2AixtC75mkVRvc6tZwPu5VHvIO08FNrOuv6Nm7eB3py6I85xuliyjJ+ZrhePq7bmP5L3kb/OjlvFC7szsl/SanEhJVkWUZSfFLGRDn39K8eNfVNxmDEXOynOzaQgxEax/QptEgZlRSuJC0lL8Lt27ehEcymg5O7yPIuqjnFIBg0RhX4rEaqKfXuAYv5FO0sXedQ9YysnlFNp/29qlR8/xS+khbdSys4vd7jTRVK3K9eIo20mY4ZkD4uwF79SSDLDBILccSnUmOFznNUOWH/8jXuPP0ksyJjktdMpxUioEtF17Qct47NesudW3fipAhpLp/ClHDDjnessWfQx/d9io3B24ivV6i+LRvQcxfOLTwhGIT0/jJayJHLkLIF6XiaR+JMKD1Qr9OiG0ulpTBiIJDFpSbSxz59ZJIcGR1+6Q1kMtQuzDXgDIPbcZFGPnzMKDQiRSuxmjJ14L7gtiSPiAvTDecVnvH7H/l95Asfw3SOzHfQbfGrZRS6qdBFhVnsYGY76GpKnllynbG1gpgSnVdkRYVNKeJ7Z39+Uq+7b3D/8TnnMXw642IvidEDvSFI7nHaZY3RcRdPHZ7D/lIUJXk95er1x9FKc/f2HdZnSzIddBqmk4qdnTl5Fna5dtuEj2JYQIxBuPRYap7ig3vfz7WP98O4ePvoSCby4ody7XQjxtU4LMpRhoaQBbHRaxhCF+m5HmnHVUpFjkDsu6niNfXn5znGJsZ7dG8f0jXvY249fD/hjQIvYxQU9Gc+NiznHhguioLz33PCIHqoQ53zZHrPZoTVBDsaPkh7TwEU3iGbNe1qjS5zit0dioMD6qNLVLsH5FWNJsPoHCcar3N0VqCzIl77AV+4Z7zBqY+fc4bhfiDig1KY42IjiQBinuex6s/HRZX1QFGvd6AUrguxpsQY2yGYasKXfNlXsljsoq3n7u3brNdn1HXBYjHBKI/RgHK0bXDNXWwQG+Jpde/cetBTYpYEvHURuQ87dchCpCKmSFtOzVHyvD8/VHT3E1EphhLB+3ERWwjv3XsPksheYP0QUmityaN4S1YEAHbILAxeTfAqegchiMfGLEda4j2HUwYMRWSgOfd05v76BMJSMlBy3sIMoVNcfAlHUCJ9ARoMJLZggGUUJ8WNYHA1ogfl+P5v+UvUpceoFuUtZ+s1qq65/AVfzOTxJ6iuX6e6coVqZ0GRF1gr6HKKL6ZMDy4x2Tmg61yaQf+dB0Gbz53l9rkz01dhjPGIUINg+kY0Y/ANhixFZtJzAhnn8KFrPPTwo9R1HeS71mvarmM6rdFamExLqjInK8Kl/Ut/9W/0YV4IzbsAACAASURBVEHfkIUB/+gX+oUYOE64xx4SHtFjJXFxp3mr+FhqcDM+T51CDfF9F2uJWES/4/sRCUmNw4d7MRAhpjkjP8L5oTS8rwCN8++9CRniekbg5jgDoVAjAzB8Xs+DiEclekPDdbroYaT3lZ663cMdFzyO9GfqcGVci3YbmmbJjdNj1GTO0WNvpdy/hK4X6HpGJ7DtWrqmDWnXakY93yWrJiiTEXpohve1IkR2U/hIOf/vG3W8qTCGlxoXwwoTm712rkVFgo7ELlFKKayEBrJ5ntM026AZoAvy6Zwr169z+vwz2E5obFB4Pry0z3q7CuKyOVRNw1/98n2+6M/9BeSS73dH0RoXy5v70a8H6cHEFAuL93hrQQzaBAEXvKC0BEBLgkbDuKxcq1B92Lv1IkHc1dogDW9d7BsR6hBcetyH/pcmgp1amYE3EYVdlTGQeBg+7vX9OpRzhkq8D0SmuFMOnt1wbv0ennZ99KAYde418WVqHLen7qQyMllEIzSETwF3GIxDPIIwKs0Wj+taTl58AXd8g9V6TXn5US498hYmO4ecnSwR22IwaOfCPaMUiMEUU3b3dilmO4g2eByhl2303DQRE0n78KfTVeL1GZ83hgE4Bzz27rAfOv4oraMCcuiAbZ0jy/LelTd5TqczFpcusbh8iWbd4rvAddC54fDogO36jKrKmbcdUlTYrguU46gdcL7Cj/tiT4MHEXs8pHbyYkZhhw63mQdJXSEkxc7hTVRcsT6GCIkdGBrnDnwIH0HWsXEJHlXyVAZ6teJ8i7eBHyFhLjIs9n5hSjJ4MmQO0uP9JRgBBvdckBHeEs8txRO9B6LSw8PzlMQlOI4iiMdVnEs6JxHEObbtGdK1qLxgcfkhyr0DLDmiczIN2jk0gjHglcKTo/KabDLDK4NzEnhWfTWt4PuTGtDOl4Il3wjj88Iw9HHqhWMw6B3oVD2owteYZTlt2yFCX+uQ5Tm+rmBaM7t8xNnNE2QVdlMnntnulMvtEadVTtO0eFOhCLuxiZ+pLqD8Krm36caO96sk7yHGx56AOWgXuQbeobXpd8T04kB2lN4L8ZLi6/C+yQCElnGB9egi67EnGCWjmXpxnLuHZZj/OD04AvjS3FOMr8THlp2B6DVMWA3PvoeWeMHXvo/r3dtYxbmVn9iMyZs4/z6DWUsGdOyZdbYjyyrq6YJyukcnBgHyqoJGQdehlEeb4AGorCSbzFBljfNgIzWmD2GiUQzeRTQMohAVWJoPMg+jW+J1GZ8XhgEGDyH9ngxFpjRWbL+DhpZsiWtgaJoGrTRt07BeG6aLGSdiOXaW/UuXmSyXrNsNm+2assrJKkM9CbtLVk75+Is3mO/tkeVlpC6PahjCZHo/V8XtzwvouLh1rIIM7ZwlttoD1wliUjHUkBnXyT32EjwD32fpSTRsCDesc46u63AugGVZFozc6KKdS7kFwxa9lgTqQtCB8D6oIcefnisiHuWHW9ygekMTzledW9cKYpX0gIuMJjQyLANG0XtLaTfuw5T0HN1/RqoMJc494EsO17W88NynePvjC6qdPaa7lyjrPVrn0AVQgkejsgIcYMBrg9QT9M4uPq/ofKz7yExIwya3BQb7JGHzGTkP979fH/zQazI+r8DHsXE4R46KDVe9DBRhYzLKMlT5TSYTtNJ0EXM4unqVO2dLWvFkdY1Shu16w/Gdu9y5fScoOdkOQXjhxRdjKOAHl7pH9ol38WhOsR9cXyY+cvEDnVpCQZQQfpfkshPaoXkZwM4R9bpPM6ZzJpxnD76KDDoHCSaMuEHvPUQ9RW2yKBsXcIdz2ZU4n5SaZMzqhH4x9+DghdWRvA3VI5X0bpQaHUzn3X9n/fcYDWDvMdADrUlrMSk9BcJW8BBTZe2V60+wf+1RJnuHZHlBZjS2WWN9B0bhAmsNlRd4k9FpQ6uDojik6lcT6zjg/BL3r7cq/CseL2sYlFKVUuo3lFK/rZT6oFLqb8bjjyul/rVS6qNKqZ9TShXxeBn//lh8/LFXc8L3IzGdq4u4sPCVUkOMzmAc0o9XCiIlFwmZiiBpFvpBeOcQBJOHakZvLUdHB7z1bW/hI089STGdMdvdoz3b0pyu0RQUxQyd14hRvHjjhYAzjNN9vbMLqOjux7VzDlgbK0SnRRBTgRKBw4T2p51VqagIRcoAjEIJVL9wQjOZoIFglCaLgi6CGhmHSHZiCEs8KhqEHJPFuoAslFErM9aGjGQjFTgh1lrEObQIWjEqlw67alKNS4d6e6E1SSDGxwWPYoR50KctewN5zhCF/5+7P5LuBZGkrEI5+K3jU1w5w0xnqEkBusO7JqZAQ8NgrYMkfOsF0RnFdAdMgRCuucQuYi5eK4ml19E/wavwo2RodfxGHK/EY2iAPy0i7wDeCfxZpdQfA34IeK+IvA24C3xjfP43AndF5K3Ae+PzXrPxUpd6jHD3O1raHfvUWLhDm6YJC6ffdUJasapqupWnKBb85u99jN/40Ecpd/ap8oKdsqYiA+9wtuV0ecJXXi7B+mE3gximRF8yNlJNrnn6rORhhH8Z8vBjI+hDZZ/3rj8fZ23MPrjBqEDfOyLgDL73YvKk5ZBlZEphVIjRSTLwIyP6lV/zn4R5a/CEXVppg8YQRFQI5xNVstM5aKVQEjgU3lps22C7NvRliK78YPAGUDR5OSnLMVaq7jMg93HHg11IAMQoTRrvkPRwWMmhqE1pxTf/xT+P5AUnJ0tu3XiRu8sTOu9BhabGJip0NyL4vEZP9tH5BKXMfSbwoNzDK8tIvESU8ZqMlzUMEsZZ/DOPPwL8aeCfxOM/BfzH8fevjX8TH/9q9SAG0ms07vvx5678+ccTBpFEUXoKtfeI17Rbz2x2yFoq/tUHPsyTn3qeg8MDMq1QrsF3W8S3GAWZqJBeJMa9caHoJALZo+0D3NSbqt6b8P3C793yPgyKGYWYcvUx7ZjCBhgttFQrkchdOgi+BC3IVJOhQk+HlLlAIvszjK/46q/h3/+T/yFeHBJVnxLPQo35D8nt73d3IPInxueXwM7BTRj99GGSRM9oYHb2hl2kB1rPf70plKH3lu754ntPI85LCXmegQRA1gqIyVEmC1QEcSAOrzN0uUBXO4guhvDoVRyvty/xijAGpZRRSn0AuAH8EvAkcCwiNj7lWeBa/P0a8AxAfPwEOLjPe36TUuo3lVK/efv27T/YWbyCcc5buCdDca/xyPrinsDuM7FHQtds0SZnttilrGqefv4W/9dvfABdz6Ao0EWO846z1Rm50ewu5nzog79HT/9FznVySsxDuTCvvjiqtwPSL9QBJA07ad9EJ1J6kwESkXNpSCG2tY9U6KAEFYxfams/PDMaBaFv9fb//OI/5dd/6ef5jV/952GRu8SqHEKhtAT79Q59VWQ6x94opNApeULnYoiL39Nw3hLPsY+9UgQxDhlk5AcmA9N/QnrdcJ0g0MuNMUwmU2bTBVU9CeGSzkhF3V5pKKZkkwWmmiPJU3qTjVdkGETEicg7gYeBLwe+6H5Pi//ez9jdc+lE5P0i8mUi8mUHB/fYjVd1jLMRo8/v4/6Lzxs/x7mgupPnoeeBFwk9C7qOs9UaZTQf+fjT/PL/+wHm199GfulhXDmlnMypioKyMPzYP/mFPj7ub8S4CP3o5g8Lc9j50wL3MnKxfQINbR8a9O3SxA0LDRkZjAHITCnKvmeEGTVryXNMHpStvQ/9MtquC1Tp+Bm2abFNg29aXNvStRu22zXNdkPXtogbcI/k+qtEkNIBW3HW4q3F2Q5nO7yzpMY68Ur0fw+GI7E1I5AYbetQdCW9V3DOoIyvNbHbdP/903sTN27comktVoKuQpB0m0YlSQeZxpsCKedki0sw2cVnxRCyvMmMw6eVlRCRY+DXgD8G7CqlUrrzYeC5+PuzwCMA8fEd4M6rMdnPdIyzEeNxPyAzjUE8FsbfepFnGOXxtqFQKnRFWuxwo7E89PZ38uV/9s/BZBfMlOlkhzKv+FNf9Fjc6eLnAoqYGlRqSAIkrzlM7txi6TGEUTVk+All1Sm8SOBiWkxpQQWFpoBX+NiVy5gEtAY1ZK10r9uASMADuqBjabsu4Bddh2s6XNPi2xbfNnTtJgi62g68D6G7DEpRAdQNdSj9ahzNOYGQKhnFBDKODES4DL6/SCnoSle1Jykl7yh5WMnbUjLyiPqgBNBoZfjIhz+CF0NnFY11tG2Hcg4jDoXgdIYUU1S9iy6nWDRtzDzp+8Uyn+PjZXkMSqkjoBORY6VUDfwZAqD4fwD/KfCzwNcDPx9f8gvx738VH/9VeakV+HqMl5hOina997Fno47ueADKdJ4hYlmfnVAaAOFsu2Xv2jUmD13j2vXrvP3ZZ3nqg7/L6ekdvBO+9iv+ME4IABwSGqGMyE5y3iLE2131oXe6uQOg7vGiYzMVj3ch1FF6wEHGbnxaSKlbdTIQeeqlAdFAmF663dqWruvw4um6JkrOKZAyZlRCkZNowCgs4QMTPRoXFr4y5tyV1VpHDywOL6ClV5SWZMCHaGB0XeKxKAN3biX2ocLowIUtvA81UD38l2Cd4DHcRIlBREXaeEuZxfZ9KFqVQzEhqxdIVmBtyD7k2oypKK/aGBCn12e8EoLTVeCnVIBeNfCPROSfK6V+D/hZpdTfAv4/4Cfj838S+J+UUh8jeAp/8bMw73tGkE+Lv1/wEO6xSynNFVOZ57IBhBtGK4XzIebsug6lYr9La9F5gRVP6y1KC1o7dvd3aZ1j64Qv+TNfw+T6E6w3Szrvmc4X3LndcHzrJnbbkGvVhxEi0t9UfQ/GUbyerMO4StT7Ybc1mQmLtRdtTaeoY9xO32DG2ZCibJoWbQyT6STk8LXGd44Xb97FijCZz6gnFVqC+rX3PhQYxXZ3oDAmDzUZDjA6pPG0oV2vUc6T5QaKHHROf5tphc5MrP2Izr0E72n8XUn6DhkA2BROBW9GxYxCXO79V30vPiEXfpLQbiomUyL8qa/9Ov6DD/16uE59JkVobRf4CfkEs3MFySs6FMq2AGQ60OndZ2Hbe70dkJc1DCLyO8CX3uf4xwl4w8XjW+DrXpXZfRrjM72Qg/AqfQwfbqDzhBUIJcXb7TakLJ0lL0PJcWYM+7s7oUW6ztl4R2sqHnrrIyx29xARfucf/wL78xlN14XFoELZsR/t7OGGPH9OqTYieRVCjKtHoUPPIBy9doxdJA/Jx+YtSim61tK2HWVZYK1jvVpx5+4xq22Lv3mHnZ05+zsLdudTxNsen0ggonUdSjQmzyjygu1mywsv3ODms89SGM3ewR6Xrj1EPZ+Dd2BCeTtaendGiEZQjfCBC9/PwLyM55w8irjdJ0BzeKGc2wjGiZ/Ru4br4R04zy//s5/lqyL71USZ+s53WK+hmKDLBZLVOBWa3piI5QSzcD4Z+mYZbxrmo9xzS72yMRaB7dFtkZG2YNIlDJfq/2fvzeMtSa76zu+JiMx733u19a7eUDdawAKDxAgNlj0GwYzQCowtwAKbHRmZTUbGFqs0ZjGLsMHGMpZlMzBjEDLGH2N9wDbIlhlAILFYArS11C3US3V1dW1vu0tmxJk/TkRk3levWlW9VVV3RX1uvffuzZs3M2/GiXN+53d+Zz6bMZ1OaXywlTaZK74+neLUqMqpd5AapGtodYKPLXfccSc467GY0oioI7l+oAJn5chK3DoAlsWJGGcZhnLpUU4/B9N1r4ZQ1oIsEJZdx+7urKYvJesuNG2gi4njJ85w733HmC+63DzWmYeTwbsCSAqO5WzJRz50B+//kw+y3NohzWfsnDzJfGuTtFxCtJXZUrTGoLT+GlLneAmXDD8YFUeNvta9YUYFkMtz5woRZfRDzRg5ISt19fzRH/0BSEKcAsZBiVFJzTqydhi/cYTkG4rGlZBqZ/HR5X5CjSdMrcTDZZGVMuG8E6zNmdZ4uEzMQgvul5E1EeazGbtbS1KEq646wlXXXk8vDt9M8IvEobUDpEXH1ulN2rV1vuM138Lv/d57LL2oeeEkS7QXCXaR2iq9rIaMjq2m5mQ0kSRmdN5nt3tIAZYqyrFxKZ5KHyO7u7tGbmoCoXEcOjglNMJkac1onQgnjj/A9ddfx2Q6qd5CUkW8p5lY5+ntM1tcc+RqDk7WmXilbTziwLcNXYw2CTOzMjhHn1mamntaACvejhvstH0341lXXIAKyWiZnnbNxlme8d0hrqpRF0hXxdEr/MkHPsCDZ15AO5kYYUs8fu0A7aGnINMNogvEPiKa8JAl8QfY02oxzsY1Hsm4HDCGJ/RImqrHICVDMEp7jbsle2+KTrFbcu/ddzPfneMErr7mGp76tGfg2ok1N+06SDG3V1PUQTvd4NgDD3CoDcRM/6jCJNkvLZNaGbvP7HuHjFOtihm44v+VGN0mz8B/EDEBVpeUQGuh0WKBoHgnTKcNIjBpzPsoPIPFbMba+hqT3EXLiVgdQQikmJi0Ew5cf8Da6BFBIlF7korJrjct4n0WRNVBrzINQir1wKk2sj6n2YuoYcNwEVh9u+75iRnb0e6rl4LQtBN2dnf5ge/5+8SU6LBOV5ONA4TJBrTr9Oro+pg9hHJ7WFtA8n6eeIHEFcMwhBElHTFKdyUthcKDaKx4z+L0jOP33U/srE3ddOMg1910E51zJA+9diAR3zgiEVLPJEw5ev8xDt92K4k8IYpdkBUzsLLwjGs87A1lZcrszNyToqysQ+Wo1OdSxU1se+d9RtMNfF12PU1mPvqghOCs2zODcdBopeMJC50mkxYE+j7ivTBpG0JowCmJiPRLln3Ehwm+adHaZwHDb0RXzztbgwFT4axJP2AJY8ux/1AGD6xaXxGrXyA3lvUNx46f5JrP//wsv2eNfXzTgAv0YiBlaUprLPaUr61f+bRHO5i42MbmSW8YvPek7BUoPaqR0vlIYiIERywuvrOeiZsnjpO6JQnPInXMU2TWJ9ykISVYdgtc42inE7YWc3zqSKrs7OzQtBPiYm5cBBRfMg/FA9hz75eJrhnFL5TjgoPYMAalpFwfpEWkhaHysDinooTSxYpSN5HrFNrWQinn8B7jNjhP1MjO7jZNaxWVTduSmoYu9ihKE1pEoI89fTIPKeLxTcNkfQPEui4Z4UmJyaGiIEMQAKWgSbJU3IAzkkOn4j3YaQzeQqWJy8BrKGFIImdo8n7MoGJ4D8KffvDD6PQwIetyJIS+hJEx4sTT+DDoaIqVX5cjtgKr9ITDGS478HFvdeS5yEt73wMDWDVehU3WzZHEm2yYN35B3yXaJuCS4QwdQhKHpMSpB+9nOd+iT5GosLa+ZiDeQtnd3GXiJ8ROkTDBhwn9smM2n/EP3/Dd+KapmQS7Tw2Q1PzEmJ5dcYFcayB5CTcFa8BQhBGrsqg8r7IlU6ln8NanwnsTpg3B5yIqY0DG2KPRpN/KFQ2tZ21jynRjzdJ3uaHrfHcHTYkQWnzTEDWZobBZR2haJhsHbPWtoVGZPIZziPcUJVnJgjCpMB5HK71WS1meL0Bsfi5TzIdQQbJX4igFTjXNm/kgorCzs8OP/MiPghP6ZI1/0Z7Ud9YBXAQl0aeeSCRJCSBSfagk0qhf56M1xuDqxaABPek9BtGRK+8M4Rex2gjvPT7FrFMQEO9Zaxu2Tj/I6ZMnSSKsH2y49ak3sL5+kKTevAzf0LYTUE/slT5FRKEJgePHjzN1g0BaSkO7eEl70YUS1jBssyckNyxuSGNqfcPIsGDvL/v0zpmqEpIlzkqEkjMCTsA5XPCEJjBZm+LbFu8c9BGNao1iJhNcaPHOW2NeSRACrpRhZ7CyTMZhghfl53JEJVzb891UEFKGzIOoCcEMZz96A3uIRkUDa7xdfr/AX/s/X8HpM6ezIUkPo4XcY6jdWFy6izSe9IahROO2EFkcqSokenoVTMbc6vBJieV8m/e+9718+MMfoY+JG265iU/79M9gvpjjmzV2ZzNi3+FCIHjPtG2RaKve9OBT4DXws//sR7OgCnXinnUPVLc3rbxWGZEMRqH8niD3xZTRa5JXzhLT57Aj61sOqb5M7MEmZNMGmqZlunaA6cY6zWRqsvuLJcv5HBFvBUZtY124NadNSwl5MQxpmGg6mp8mxmIGiJK+VWrzHBgMXqFtMPpZKNAVSynZiupt7b2eJUyTus3JUw9mcdmscq2P4US/0CFjY/b4jye9YYCcBag3nHkQnSpd6nE4UpYr8yR2tjc5fuwBuq4nqnDw8BGuve46nA9Mp2vmZs9ntE2DOGNL9rnycbF7ksn61XnFLEJsZdrkD9cSY6yib8W1dMVgsCczkQ3MuAlTefcY4LSJ5mxllTytlJx9sXSceMEHT9M2TKZTmnaKK23qQ8A1jTVrdlQ9CQHUmUCqYYRG/aYYsr3hnmAdowpmMvKcyvkMRz/ynNiTtVjxB4akoQhnqyUJI9sie67dAOpeGVcMQ47PyxpLTVk2rTWl6Z0jLgUfhKZx3Hv0z/nzj30cH1pCE3nqbZ/EU266iUNHjoAEGu/Y2twETFAWwajVITBfLGCdTLNe4sQm+uD+jlyBsaFgMAIm/fgQGsPj0MhOCFRwOrjr43fnCKK6/F6sHLtpGiaTCZP1NSZr66j3LJZL1Dmk8YjkQqzsGZQiMXXZ1KnxHTwyAkkLJDg+AjMMUtsEjvAIhequ6+iHjLwhdDCC5fKNDYwML8oIe7A07cAQlfGHXgq24SIfx2UHPj7aI+bklbHZRjF87j7lfLAHDul77rrzjiyLljhw+Cqe/sxP4fDhq0kpMZ/tslzOjWiUVYFKUdOkbY1U9OCdhFzaXEqqycW94yWu0mdkNBnqawwTceXf8LrqAMRJ1lIrQO24eQ35decE54fuUN57fDAZ/VoVmRIOU30mWW7fuAvF7xG7TuVf9hyKEEpNm5aDzKPI3Q0oSDYWBWPQuvvRJ5XXx7NHhj+zl1KMXqZ71Wt54sTJs72YS2lc5GN7chkG2fOz/FlmEtjkVMvvW4FVMD1IlG4x42MfvZMbnnIjLrQcOnyET7rtNtYPHAR87mzVE5pgN2K0BreL+ZwmNGiC9Ws/GReyYcgKSPa5VJe7xsyyurrD2MUeo9b1mcHzyHjCXu9hrLJUMx15lK7frnSeFo94Z2FDzhSMy7KLMTOWZKaQ5+tZcJtybdNIobocaXH5KxQrw3WovA32zpHBqxobzME0DiFTvY71fcN1+eP3/k/OHsNnXvxx8fAFeJIYhrGqT/lZU525otGJ2kTIN3fbGrNvvpghKE4j9919N8961qexsXGYo/fv8Em3fRJPuflmQjutn+WcWJ4/KX1vpduTdmLKSaocv+cO/sbXvBqcq9y5pJYSlfEqCdS8WkEEhfqN1TStkj2PsQCJpTOlVmStblsuQ/FmvAjBuSzSKgQXaJoJk7V1msmkTkDvnZGznKdt13CuAUrNQ6Ydj8HFfCaq0cIfMX3HIjtXvXcGYLRI0aekmeswhFJl2zF+soJFZCORqne1Z6KLhRJd3/MD/9cb8rUyubb85dV29Rd7FAzkfNLx57fDC9vHk8IwnDVGQFwZWh+D/5py2XUIjm65YGdnm6fedjt/9EfvZW2j4dDVV3HwyGG7obCbtFsuKSt3zCSm0AT6rqOLicIQ8KEZPld1REIaew7nGqtrZAUr2fO+Fe9i2CZVebfRWzPLMzgj9ATxOAXtrT9lypL14jwuNLhmgrhghVEZGxgHN3XnMsCCrsY4uo+3s/cUK/AxMgr7XQUqgDkie1ANQnFL8nOqyote8jJiVs4eF8iNjeYTbVyoaXlSGYaxW10m5HiM5EaJXU8TAilFlotdFvNt5rs77O4u2NzexbUtBw4d4vCRq2naibnSavoF3jnatiVBJf0oJgKbRoahRhG1OUqRMxulJMe5yXoixTvY3+Esrv7YAFavIqWqyIyCq6iANd8J4pEkpGWkn3V0u3N03pEWHU6F4IM1z/EtiOc5n/9CC4eUDO7Z1bWODSmDe+VENTMWUwUaBxBAqyEbn1c1fJJNToZi9jtvHW88CkXyhayPH/7BH1j5TsaG4dLwFy52IPEkMQz7sSRLt+jxUBkEUZ1zTJuWEIQU59x154fY3tni/mMn6dVxemuX5z7veVxz3fUsu4hToQ0NcbmAlJhOrPOUqhJCqKCeCZdAHzWXM5fPLNJrRS69YAFnG4dh0gxeg+ZzqrH/cPJmcrIH1PVFKzL3i1DrDuXVWceoBKmPvPDrv4nF1g6zE6fpTm2iW7vIosNH8ElgmdBFz7v/03+km8+Iizl0HS4l6x1BmejZM8jNckhWgl0k5zRZ+/lhtR55G7ACbNbvaUTgWvUQxtvsxSDsj5QSf+ebX51DH2NFFpk8WNXneDKPJ/VVGDyGAbQqw+FYLpZ44Mzpk5w8cZzDV13FHXfexekzMxaLxFOf/jSSCIvFEsGIOm3bmuipQNd3tcS37zv6PiIusH3iPmJSQuYGrPRKKDn1elfL6jZ5rDoQe0Kjvcte8RRqam7QO9Dcyp6kpN4MR8quTOw65tvbLHd3ibM5z/uSL6Wbz1nO53SzOf18Tlws6Gdzutku3WxGnC+g63HRUpVutFKPVahrOMHgwp8zDM7eglTEcR/cyE5s9OdoZyMYos96FAXPGSjHUkHZS2Fc7KN40hmGvXUI5OaiBnzZNg6T7ZKU8E44ffJBjhw5xO1PfSp/8J7/iaoymTYcOnTYSEc+5/NVaJuGJjQIkhvWmIEoN6cPLbjA3XffQ9tODICUkjWAOnVq3E69WVfy8+U/Hd3LdaLlc61Pa92fIKSoaMwretGB7BMpZjEXlF/96X9MFzu6bsliMWc23zVj1y1MyGS5oF8s6JYLUhGInc1JszlpZirS2vU565JBRUNERyCrrpyM7jEWxYAMDsRQRVrRgIzHnJ19GK7AcA2Fd77znRSuqV2SolFh26Z0CbEfL+J40hmG8RARtvlzUAAAIABJREFUPJaKzC1LgUKpiTiX2N3Z4fTmJtffdCOb29v83m//Pr5tuPmTn8rBqw7jmxbvW2JKtNMps3mPAj74XI7cokmZTlraJtC2LYuo/N1v+yZUhvZrdTLXUEcrBgHknm7jFW3P7Gd4rSgij3XiUoxotF4QKes/fvl3fDeveO13kRLEPuXSY8lNaxI4IUysinI+n/OOt7wJ1ydctFLy2C+RmJCY8ClB39PPZvSzGd3ODs/7wpfzvC94CdrnUEEEdc7Ykew5fMar93CGK49xCKFlH6O6iQGYGRmTClCgwPd87/dUz6IUfIlkFescajye42IUSJ3PuCSZj49246r9Lr5zDtThaei0A2c9HHu18usUl7SN46N33Uuzvs6NN9/MB37/D+hmC2KAv/hZz+HgoSPM+kgIDaopu6mB+WLJxoGJ9U6IkdC0pN4MBh6iwHTdKg+TmvqSYqur5hUwqdoqtt+1yC4wzsII8zqyq529bas4dDinuW9kKklFgvOA8NYf/2He9uO+rs4x9jRNqBOq0K/FW/dm+kQ3m9HHyHRjw7ZLiT72hqfkegxTmLKipN/697+IWyxpJlOSI5O+soR96caFhTRWdHW2YVgxDyMSmFSwOHsaxUtYARFHoQwG7eaLbOzN7DRqLp22++ICb7CHOYaszBDilUMuIO6j92EXtvmT2mNAYTZfZqUlzPVPxmfw3nHmzBnu+OhHufmmm2gE7v7onUy8IzSBZ3zqp1hmIfePFAHxjslkiveBmFdZ732tm4gpEfsIYmW+H/zQh20VHQNnlXyU29gVgLFI0JWJLwNTIN/m9YbyITD0U8yrpSqFiFQzCFjzF43GLYhdR991CHZjxD6yO58Tk9JOppw+dZpjR+/n9ImTpvQcI8vZjOXujG6+oJ8b8Cq5rfw7/+3PkrqeuFjQzedoHyFq1lxcVbxWdFit9yQU6pMjMHYgQA3egux575DoKPjGiLZdjN7qJ/D45wPO/rzHRBPqAnf55DYMAq51LFNH1ESSHhXrpLTY6bjrI3/O7bffzs3XX8fdf/an/Pav/xouKbc+9TZe9NKXg/MoNoHAyE3T6aR2eEop0jYt3gXEGxtyOZ9DUro+8i2v/nqa6bSu8C7vr/IBMlpfek0O0cMorNhjSJzzpJgbyeKIfWK57Fl2iWWvzBY9XV/EWSIal2jscCTmiyUnTpzki77t77G5uc3W9i7bOzNOnTnDgydP8YrXfS9nzmyxmHc8cPQB7r37Xu67+15e9M3fRj+f0+/O6bZ22Dl5htmpTeJsQb87o9udsdzZYbm9RZztoosFuuxz9sIMY9K6buZ/AxYx0KXHpLQRWCiDpwQMzkXGNiSHCb/z27+DpoSXTGgbgc/jkOWij5EX8Sjv9LzHJRlKPNrjnBZYbElJmhBv7m3TNHjx3PvxB2ibCTfecitpNmfz/vs5df9RIj1/4dOfxa23307X99XrUzVpMB8CsVsg4gnBGrhEtfSj84G+W+K0QZ1w3S1/gbdtvJnN+Rwr79axmquVRZ9n+kxHK2mp0XAC88WcUydPsbO7JHiHd4H16ZTgPZIUYgZGnWN3MWc+X/LG13wbX/FTrwfgLa//fhMy6XtU4C3fNSF5z/bcOk99/Q//IxbL13JU7qMJgQPrGzkEMuJQp0qzvkbI9SgpH6clZRvEu+w1raZZiy+k+XuqvIeMKUiWtDvH1aBoPGTaKAB333NPvr7FBBQ8QSj1lZfEqKWvj+YRXZjL8KQwDA91Tbwogqk3Oe8QB7PdXba2t7npppvYWF9ncfIod77//WyfOYkG4bM++7OZbhzk1OZmRrUB7P1t07Az20adCbMMt6C5+P1ikRWiHbtbx1j/Wzfwr3/6R0nd0mL6HGc6N+pQlcd48oz4SxmElwrGS8EeYmS5WFj/ya6j6wToUHV4cWjfMQ0e72xFbqdT8J7YR/7p33stCIR+WQHSn/me70aSMu+WeVFTfuY7X0s3n6FRWZu0Vfimadvcqcrhu0AUm6JJlS5FfNNUTEGCr6v/kKwo/gMVRxgPZQw36PAlq47eZQpRSYQTJ07wxjf+OCiDglbdbTEgl8p4tI3ChY9L6Wo87kNItI03gk8yZHp7a4uj9x/ltqc/jcPXXM1yseCuD3yA33nHb7JY7HDbs57G57/4xezOF8QIfRdNL1Ej3jt88CwXC3Z2dphOJpXu3Hc9qKNtPE1jVOL50lasg0euhtBQJcnKeulGMfUIK7DN8kQqfTFcca2pnaD7rsN54chVh7n2uqtZ31gnNI1JlEnCNZ61jTWmaxPaAFdvrPHtP/pGrju0wdUba1yzsW6PA+tcd+gA3/RDP8zh9QmH1qYcWJtw1eEDXHX4EN/6z97EMz71mTz16U/jmptv5MC1VyNrLVuLOV1KzOczNk+e5Njd9/CRD32IOz70IR544Fhtwlv0FEuznwFUBAupikJz6eSQJ34+75UQYAwyOgHnQeE33/Hf8M60J1Ncmp7jWXNvX3Dj8R3W44+LHdQ8OTyGcwwFlv2SFHtA6OYdm6fPWPpq0rA93+baI4f5rTs/zL333cPOcs4rXv5ynnLTzews7T2xdHd23gDMZNkHjUrTTNC+z7x8xTu1fpHeE2PmNwD3HD3KkY11A+5QSyu6jNpXUJKVe2XkLJzFbwh51ReB6XRC7BIpzplMG5q2pdRFtE5oJw3aL3Gh4fChgwCsr01pgm27tr5OF/tcYQo33nQj7WTCvFuyvr6ea0PgJd/6d3nn//Nv8CHUzMJsZqK3s9kufeyRtuHwwXVc09CuTStpUcQIZZIzAilPeksplhPNxjI7BFqfGgRgEaGKMOUKUUX4kz/7U37sx360XqeUFOes3P1ij30TcJcAyeoJYxhWCmEKgzDH3CvirzWHPuTCJ9Mpi9mc0ydP0gTPNddeh28C/awjdQve/yfvpY8d11x/A89+7vOQpsUla/g6mUzNsGhisVgYlXq6zmw2YzqdoAvoZgtCaLMYqfEAnHiCL6Sq7+cXf/ZNLDa7nMIzaTkyoJji3mrJYRSXvpxXjNEqE9PQ69F7oW0DzgkpCbG3LMRk0iCYbsSBjQOsHzrEf33Lm1geWDKdTplOpybTlhmdovC7bztAaAK+80wPH0JnM373V97Ku9/esNzoCSEgIvRdx+GNA3jvmC8tnJHgOHz4MCLKbDZDySrdKeM8GD5S6Mxj4HGMMdgYlKIqFqEWbNnEJ4Oayl133UXf9zS+xSGkPXUUK6Dl4zgn7asef+Bj6SVc2L4vvsl8lEbhu6/2YSjx5MBsK7x96/qsIIHt2YzTO5v49YYDV1m15HJ7m6dcczV/9N//K+/+rXcyObDOc/+3v8qn/y+fQ8xiot5JUTfDNR7fmGBswgCAlAZWnZC1DLwbtcUTTp9+EFE4vbWDb9oqwFIa3trJjM5zL+A2xiDyufWZP1GicxFovacNgcbBpAlMQqDxgcY3tGGCk0DsEsG3HDx8FWsbBwnTNSYbG7kByxq/8+9+ERcaQruGa1qWUQntlMn6AXw7pVlbA289GcS34BqSesJ0g/UjVzE9eISlwFIV3zb40NQsjDMEdxTzj1iQxZTn+ayjbeo5ApqK7XDgPC944YvoY8/rX//95kVlo+lL3UrZZxkXf6F+zMaFloddOoZBRo+HMc4FUNebSocSW1OAtq5DKSqb2zskgfVDB3GtcRrW28DROz7IO9/+q2xMG9r1NdoDh1k/eDVdTDWOJ8e/zmcWY+7xaCufrYJmlIoH41ZONyZla/sB/s63fichd2wCKM3FdeVc9p7bnt8zYaYoJpXzFzGvofGO4H3+abqPJCNYoVZRqnniiDjzPDAVpz5GYjQuhCYluEARc3Fi7eOFosdgqVe06NCYi59iMoZlzoJoAVhHeMJwVkNR1bikZb8SieE1zaCs7e83/8uv84EPfGC4pbIHYvWkbjAtT2CDUMaFYpmXjGGQ0b+H9f4M0pXJP0xAYbFYsFwuWS6XdF2XH1YDsLN5mulkjauvvZ6IZ3c+58D6hNnRu/n5n/gRdk/cT7uxzlNufyYv+pIvY3sRmS8Ml0hdR+yWViGIpSWTCDEpzgVSH3MzV8syOOfwuMxLiKgzY5FpEGxub9PkzICrkmrU3P3KyTL6ss3FoIq0pNISZai/VAHxEBohBOFrv+8fIpKI2hNTX69HaVGvyajT3XJByvL33kn2tqJxMWZzlrMZcdmRlh2FUVSa30bNxdexJ/U9kpTWBwMRk9bVXXzGF0Zsv9J1amwItKQYc5o5F2nWmoficNh3Idx/7Bhf9be+ghg7S0U7WxCiQlQzejAYGdExt+EJNi5wWl0yhqGMh0uHPqs4Kq+UIqbUXGLfQhgytzKxtjZl0k7pdhPSCUemhwh9z2//xq+h821Ob56i854XvPhlPPPTP9Py+apo6u2GTwOCXEKZpNa7oWYQ7FU7TiVnMTKq7h1NaDh+9KP82Z+9H/EeFzy13HhcV1BIPdXlXsVUClrnxnlMMnlIqPUTzjve+hM/SGgDZJyjjz1935NSj/addahOSup6tI8WxasatTv2uazaMBPRTBpShRTRZB29RMwwxJj/JtJ3XTY81NW9LgYyGIQqa1/+lTqJfM4rY+QpSMFoVHn3u3MT4ZTy8ciwTUn7XnpT4JIYT6irUiZIwRtK+XAIIWsiuOpVmApyw9rGGvRKP+s4FA5wqFnjxD33cMef/CGTVrn7+L187ktfzue9+OUsEtUJda60J6PuN6nSK4Cz+HnEQxjXAFjfSgVXwptA66d81Ze+lAeOP4DzARhKg/PJjSItWV0BdIjJh0VVyPru5io4e6hzJOfAO77sO78X1zRIE5DgQROp6+iXC2tf33fQd0iM0JvB0NjXVF8jjoBAfi4tF2YUYk+KHV2/pI8dKhEJ4IIgYsSt4gkVPKUItQ7TP1+nh4ovdXQ9FCOROQvBzmxu8n3f/z3EFPHiCKXHZwGlc+cr6vUstSRP1HFhntAll5UYV9id7T1kxhu6cp4VkU8j3zNPXlWjBEtl17m6ajjv2ZnNCaGlCYFJXHD8Ix/nfe/+bf7sQx9FvfK5L/8bfPnXfTO9mzLbnjGdruV4GvAmbuJy1yrte5aLBZNJiwJ9igRn/ATvA6rk0mNys1SQKKQ+0YTAiTNnOCJbbG06Dm4cIPaK4QxpBZ2XDG7Wq1K8CIanFbM9tYGLiGkoltoEgV/6qX+EEyM7eTXBliTKsluSYiSkBuc9i7gDlMInAY1on4+olFF7Z6GDy6i/E8s0OI8PHlRrT8pi9Aq4mEYxg+R7oDKQVFeM6nCfkO2B5NBFEBcgG4DPf8HnmaeRTHdT8Qbcysi7rDngJ645GMZjBD6KiBeRPxaRt+e/bxeR3xeRO0Tkl0Skzc9P8t8fya/fdj77Pz+9PakAUv2XST1OrGIQijQ6GfQzllsIRlf2jcnBp+xiox4nCceSkw/cxe//9m/y7nf9FgtpeO7nvZgvfuU30EyP0EehaSakFNGUayO8ZSFKCzUnjth3bKyvAUqfS5dFBO8HKTecEJOVQROjtYvTSCKxefoM73n3e3DeejvWPg0lxTZCzMpUGjpOlb6NUPo6jsG8mhmpJCCL11US6jEwUOCFf+fbWcSO2XLGfDFjOZ+zXMz53K97FTFFlt3SeljGnue/8m/xOa/8myy7JX3srb9likRVXAg47+1YldxLE4psftWI0NFKnT2v0Z1RbpAsIVfozsOlSPZlmDeEcM+994Fataz3DTHlRSMbhQLOGqZQLtMlRot+iLFXkex8Hhc6LiSU+HbgA6O/fxT4J6r6DOAU8PX5+a8HTqnq04F/krc7v6FFyWe0oqQhHheR2qh1HHtqMikxD3hX0ohCUUIyfMHiSkPasRtWoG0bXL+g6Xf5sz/8Xf77O36ND374/XzhF30JX/RlX8nNtz+TxTJayzWx9KQWKTAnFqOT+QMx0viwwqkovRTKAlhZioW5KDZZYzY2mhLPu/WQFWBNGgtTnKH9KVmTF2RocT+Im2iWdmOP15if0ALYDSuzvWrXI6IsiSyx41ikjt3lnDM7m5zaPMXmtjXR2d7eZGvrDDs728zmu/yXt/xz275bsox9BjmNAVqYmynjLmkUWtkxrnoDY+MwHF0x95K/75JpycjNSrhh27zs5S81bckMrjgXsvcIhcL+aJf2P9HGeRkGEbkFeCnwlvy3AJ8P/HLe5OeAL8m/f3H+m/z6F8h5fwu6osM49iLOEm4d3eSAgWZpicbe4txklYkxpVr/L8qggowwcdC6JWfu+yi//5tv562/8H+z0J4v/YZv4Cte9c0cuuYpnDpxmhA8KfX0/RJSrByDUtGY1NzvGKMVYXlvxigXM5Vz8hl9dyLVMCGOLkZiUpqmoW1adnd2eeXffi0pE4t8Nkq43GVRSqZidFm1eFI1Qi/fXrmydRqWGLswK4s730ukd8qv/MyPEz3EAL1TUiP0LvHLP/GDzLoFs8WMrZ0tNre32FnM+bV/8yZSEAgO9daoJ660hh+t7zIGTMsRjvtKFLJWBhSHO4PsGtZjLibBtDTN6P/iL74VEV/RAvPWLNQb7qMrRuETjfPFGH4S+PvAwfz3NcBpVe3z3/cAN+ffbwbuBlDVXkTO5O0fHO9QRF4FvArglltuAYaFbiAlDXZrDBzl968coAsNMd9MER1UlJWcbsuudFA8Nll96vifv/8/OPaxO/jA+9/PDbc/nRf9tS/jOZ/zV5n1DVGFNgi+iwQSneuJYi3k93o1ZTUfZ0LK3yWNWNa1sbQYuNrCrgkN3jmaYDfxx+68i0/+5E+maQK9WFGU5vbsxjNIFdGvE2iwDOXCDb8OF3/lOlpmgLqyAqgzZMO1Du+MfakprZ63CK4NuCZYlWT2hKR6NWXxH7QShlV79furcb9U+2Cr/vjgJR87lhI2pmTuBaHw9rf/J37iJ37CJr9z9Zo4GQnAXLEL5zU+oWEQkZcBD6jqH4rI55Wn99lUz+O14QnVNwNvBnj2s5+dF4zseuYVdi+ludxQe70HkSyCkpcR5xzOWwjhfUD7jpBb2C+XCx48doyPfPjDbJ86xsc/8j6WiznPes5z+cznfy5X33gLc3V0fWRtfY1WlX65S9M4Eo6lRhrXVHxjPMFsUkldpaw02M4lacI7b+5tDitKWKNOaNtpllUzgO8nX/USXvPmf8r/+9M/wvr6eg1LcEPuvQKq9Z5XRIf033h1rL1yR1/QSgyqVq8QXKjuvzit2QPv8+/O4TOZK2F/u2CGQbInVMKdum/JR7wnDDj37SKMw4h6Y408nHIN3Ciz8frXv97wnuyJVdCVcbqz+hL7fO6VUcb5eAx/GfgiEXkJMAUOYR7EEREJ2Wu4Bbgvb38PcCtwj4gE4DBw8hN9yPhmKbLrg5cwbFMVf4obKqBq2EOfmULem+AJ0cg1ThObOzsc29rk+LGjbG2eQvueza1tbr7903nO8/8Kh6+/AXEti77DS2I6EZQ5S7UCqdhBVGu0YrF6NEQ+E5dEpHIl6jk5gWRGorSn18LrxWUQTEAMaJRkUzqlhCPxz1/9Ur7yZ17HL73pjYQm0Iqw7AqRKF8zdZig7TCJyo1fpOgrcJk9AyqZqOAdDpcNKuLwTSCMmuiQjZ4U8lhJz2Y5thBasm2xyVtW9rydZVMN5StVomXSlgMfewbGGi0dwYfrmQpuwGjfCTa3t/jcv/L8kQHJ2RLMq6nEr5oNsQ+6gjOce3xCw6Cq3wV8F0D2GP6eqn6liPw74BXAW4GvBv5jfsuv5r/flV//b3qeipdjkpK5nZnWq6uuO3lVqHGqKs4pId94KbP25ovIfDbn/mP3ozHSBoc6xzXX38AN117D9vYuh66+hqtvvpXNnRnSw7SZossF/bKjmbQkhCjmmjrNx7QnPi5MS+f86CYk38RYT4m8cCaKNqPU2FqB2MVMbZCKqEtOtO/u7nLw4AGrcOw7NJWVshjNIYUr2TqMV1UZPYpxKAVL1u9C8C7H4SJGsvK+Zi/GBsE5YyomVZwW1ShXV2JLJ9cPy99VfXX/e6x8/+W/8e0yIJLFHuZravtLKfKbv/Eb/NW/8vz8WSaHN+YAD1Tph7r7Ho9x+cQxj4Tg9A+A7xCRj2AYwr/Oz/9r4Jr8/HcAr7vQHZdMQ8EIxsSlAuyNSUsIuJRogVaU1kFAicslu7MdptM1Dl91BNe0XHXtdTzlpltpNw5w69OfyZEbb+Tk5hZ9H2l8gyZBCDRuQuqNWhvFkQScJFpN1i/B+XxsBfX29XgtEip1A2Ywgg8VfzDQ0jwMVBDxluNXNUHZagQTP/0NL+HrXvv97OzsGhjZtGYgRl5AAflKl+gC36oMTMAyL6UaMTsH62rdICHg24YwaS3NGAK+sdLrtm1p2gm+aewRGkLT0kzsOckUbhcae18O4bzztUFuMZ5DeLEaTgyp5wJESjUy5bqKDAZMk/KCF76c//E//j9+8PteV0O5slsDVPPeZfDqHkkK75GN7DG50eMhDuGhUo9jb3rv49EaF0RwUtV3Au/Mv98JPG+fbebAlz6SgyqhRN7fymtd16+EFlpAxbz+dl3P9u4ui860Dg5uHMAfDqytr2VQMOCdo1ssUIQ0XzKt6dBoq7cX68qkiojixMIXjxDU0+2fZFsZe0HSmD0dl41MWRVdVk62SWRhR/SO1CkxRvq+56e+4Qv5mrd8L2/+se/nmmuuoWkci/ncsgoONPehrMSvAvIxAv+kdLEuhCwrjgqhqV6AD415AN7V1m0ymthJSpp30FKknIow8k7G7jvDzK5ezeAZVmXnUfSv+T0FVEzlasvgYSVVfuWtP88XfOv/uiqzX85ZqR7ZxR+rHsz5vuViOheXHPMR9gEdywsKkKrewJgI0yej6SqKbxrW8sq1Nl0jitK21vVJU2S5XJr3kYuBGoe5uxqJ41SYKhCtLkBKJsFBzvVDgcL2R9jHf9dsghMGPsGA1jvvgWQrubdJWJSNnbcJ+J9//df563/9FaytrdG2ba33cM66UGuK+4Oz5Zhk9ZjIx+N8NgA5LPIuVK9GKqDqkSpRn3EG57NUvH1eaU5tOMLAqchN8ijBVO2ula+FfbWr1yxBvU4FZKzAo9oC8AXP/6wcdg7ZoGJkLimH/WHItF3soOOSNAzjG9tSacOK4XywZiwp4YqkOhjzLf86mWAuZ2lxD8YyzNuGDLKJCJGIzyufxISoTYiUgTtXG9EIiiM6N5Rcc+4vsMbnwzN58plhMXwgIeJI2M+YYo3xEY/3SkoGUv7Tb3wh/VMjOzvbOOdYW1urK6e53SPeno7FVfdIn9VrXI5pCEnqej9y+0eoYt6fs/CnGjUD+SrpoMx1ESRrQCctx2JXZFUDQeo1KnmIworU+v1L9kgy61GVv/SZn0ItuCrsUKB+zOU+LrJlu+SLqCrCXG5UJcfnnqZtaNuGtmmYTKeEySS7yeYt4KxXRIqmLRh8wIknqZCsFIrkAkk8CeP6C1YtaFRqExQVPKgBbL32VnNQD/BcxyyjKscMyukgtOLETASqmfWYWC6sxDkv8fgQaKcN3XIOMbLWTth9/7s4dv9R5vO5FYO1bcUknPerE2o0XwoQKuMDruFMEY7JIZW3cAJvIQZujGOUkCDzCFSzWE32MGq6dPjwMrHzhRge2bysRBo60KYLV8J0He0ANCm//O9+eTiFCmvKYEQu9qx6FMbDOYNH86wvSY9hryu+ynBM1UAMmYmU6wiVHALbBBdwbUPfR1SFrjfSDhoYqEE+N55N4GJuhFJgKw8EVEpqsUfpzVCUY93HZRjH0AMOYoYhlrSdkywrYOEKeGLfG1NTGdiNUKnFoHjvWN71XrYOHGRtY4Pp2tR0FnPTGHXODFudbHvQeRmAXHFaJ2S2RZQy6mJckNJhqpgFSyU6LduWFd3OuXhadm10MCblMMbXq3oXWIRBMZz5yRxnpRKOaOI97/kDfuwN/8A4K5TjHhGqGP93GbsODyON8miGH5ekx1BjYgajUFaQQm8dU1xTUhMU6ft8M1t3qK7riDHlAiuLW51z+KYxBSJcBsYKfOmyTltBgAcJNhsFUBv1N9zvyysx9z7bjOs+EJBkwqwiWVg2xToxNW9fMjApmeezvj7hPe95N/PdXfquw4/TiQUcZLUOwT5csoSCVpzGBF6K2klJxWo2hGVCD8ZEs25lxUnyRC7E6rItqiODoOXHyMgP2pvFeAx1H1S7UF5NKfG+9/0J3/K1r6zfRfk/iaCSFalya7yxx3JZjoeTNXkUMy2XlGGoaclyE+7FGnLsP+gtlBoETzuZ4pqWJBkV8AHxDZqzANboRFHtSf0S1SVCj3fmXTjJnkgSRB2iDoqXoB1IxIkjuCYDcYPrepaHUM5lODFi6lFincDlvhUHk2aKxMR8d2b7S0rrvB0LgaTCMkaiRmLqiN2C517r+IbvfAOnT5xkvruLc87qKorQS9aWdLk2Q0qpOANIpymZl7LsSTENK7/aNdCYu2xFIKr1w1CMX6EMXopG446kQkY342GvpRoa2Ic7kpL1FyR3wba/k2iGO0bXNCtG3XnnXXzdK784F1gWY2aYjIYATUC1J6bOPhNH0pHK9iMebvTYZ4iCpH0f9XvYJ2V6rsdDjXO+71E603K2l9UQlbMM49kXcjXG3WcnZz/Oev94V+O1bU+Ys2dV3i+XvPfLrt5ysQw5sOm7Ptcj5D2rKTjXt9aV3FSRAL7pu3+I3Z0d5rNdUoq1RsO5TFJyRaylgA3587OQjYniRmLsrRR85CWUjTUfy4rBrr9XbbW8fXlbqoa+XkgZf3fFmI6NaAEZh4pMVeW9730vX/6yF+z5bsadJ4obvYJqnvU9PLKho8c5Xn4CjcvOMNhqfPm5iCurYAHYsmEw2rKy7Ax8NBk2NV1IHepGSlt7VeM4/Mw3vRiAu+++m93dGfPZDBGhbdrMT/D1UeL5lSxPNjRjxav/pP1fAAAb80lEQVSilWChRpmcKcvhZTm72OdUqTFMUyphxmC4dLB+1QYO4KU9Ww2Hjqb7CHhMKfGu330X3/jKL165lueag48mwWefvfOQhuHyuyUfclyS4ONDj9Xw4nIZFsaUVbv4Hq50f6PvO5bzXfMSctiTNNLPjbwUvDfeQA4JnPe1n6TozwLwL//R9xBj4tChw7SNpVZjn0gknEru2yg5s5gnbUrmzjuBaiSiucFIzVaW1wz4VZzmUMBbVsI4CXunTgEKVv2qGmplo2AeCbzgJVa5/463/3tSSnz2s27js//CbZT6kvJ1n+tbH3sQwzOP31J+Od2Pn2hcdh7DxaO0PrJhq5mBc8M5gCbBiyMuF/R9Rwnkk6TcNEVX3lMUmMok/Rff+IX8zN9+CQB/+7t+iPlsxvbWFrGLtM2E0IRacOSCr++HYSKV4iQLLcwLsHQkex7Fz0/19cH5GFb+oow0nHf5nQqJrtB4s3fx397+K4ia6M0vve1t57iS6RxTfT+W4xPMv38cx2VnGC71oXv5uVBTlkMBWGF2kjUJheViRuwWOHJ3bMCLYxJa414Udz/n9rsYcxdtMzBvfvVLAfiD97yH2e4uZ06fZjaf4ZxjMmkJTYP3wYxDyV5kLQOtIU5Wz44JjQb6xT6S+j4/F9H8euxj3rY3fKLvs+xdKTPP8m2MnPBMjcxORA5DMnYREzFFvv2bvobnfdrTeOMbXjfyFGQFB6pLgo7NlnlgK68/mv79GFDc7+VH75MuiXHZhRJjT2H4/bH4Ws52Qwe68SoIuSI88hCHojoCOktc7QTvhNnOFqJGtU6aKcAKiFRR26TJ9A7ycykleqDxNtne8uqXEW5s+IM//mM+4zM/k9nuLgDtZEIIjYUISXLmII8iqiIDiSipWh/NClim4fjtkHKmA1SthZ3LDWpKxy8yjlLmbSVIqaIxYxHDheHBBx/k/3j+s/nsZ33LwP3Yk+2p+2XAO1Uh9gnxCaSksD/hV/EJRzmGlDGTIs5jL5bvcC/Y+cTxUC5rj2Hskp/fduefFhLZP2YsK335fb/P2TvGgikDsm9EpszOZvvM6cy4tAnlKOpQWiXRwYqsxqkvTaVvQzZYKfHcawIf/OAHctftbebzGYiaUQm+irSWn4NugtbP1NHErhkCBnAw6dBGT1VJpDqRis7lCjdhBCjGnMI00peSovK/P//ZWVfTRH2Ha5kxkXxZh/qYAc3oY2+0aynMR2XkSDysUcOsDLBW5mi+38r9cTmGteczLjuP4XIdRXzEpB2kMvlUTcTl9IPH6RZzgnM4AqSl3XTBWUgvQBJStHLd4LyBhlUAxiZvFNOd+IyDHf/pP/8aX/iiL2TZLVku15lMp0yz9wClQQ45JZpnkoipRTsZTey0YgwFwYsZB8ldryqgmSTvJvM1GBkUyBkOrZ//nGfemq9QqdQcBFWg6C4YcFtAyuGaulqVevbkfGSrd9KUqepWxCbeAGHbb15PpXwx8oSLJS5rj+GyGgJkwo1bIaMY1WfrzGlr6JIneuwTmhjIVHnrwvRcJc2QXXczDl3q6LXnZZ95C7/y73+Frc1N5rMZX/aNr2FnZ8easATPZDphMp3QthPjP3iHC76KziZyAZT3eXKUhxSOY55ApehplW9pjWSK6rtWo1A8h8/6lE+yS7PC8dgPo8kGRo1AVbYp1Z/i3B4Ow3A9Hu4ox7tf3cuTYVwxDI/TEC2rDINfjLWyQyPbO5uWEoyRFJXYaW44m4G1Uuikub9ibiLrxIOMyrQlpw5zFeiX/6VP5bU/+JPMZzP+1Y+9nt2dHba2Ntna2mR3d9e4CRoRJ7STlrZpaNqGpgk0jf30IYcgOewwwNI+q6hWK9kAYE8Uw1F4DWO26p133cVznnnriht+7nknNZbfy4SlTNqCk5SQpW718AxDMbDlc4qW50O/6WF91CU7roQSj9MYJ/9sVU0gjnbSsnXiXk4cv59GHH1MxoNMkuXn7Y4z2TVH3/V1NSx0bDMeQ0qzz0pXLjfF/fnXvIKv+skfq8fyr974BmazGc57Jk2bORIuA5u5qtINegwuKy6XcmwDRLMMnLOakoGikdOyaVCJVlXmiwXv+t138Xe/8St4ztNvPWdcXpoR1+umJRgZiF4uF3YZHpJwPuNMedtHY7Ur5+sy83SFaFatgKy+4Qk0rhiGx3UUiD5PbGcT/szpE2xvbbLuhD4acFbi/qoLIebCS49NgGSGZVxqLBStA60pyHK//txrXmGroA987T9+AwA/+49/gNgXGvYwySULtxSlJ8mYgc8kq2ogIHsHpiehFSUcwEtV5TnPuBnv4C99+icjrJSg1aHK2ViBZnm8UYpwKGwrQGjaY2QyMvhwV3AdPBML0wzTSSPwcV/DsJqouuzHlVDiYYy9Sao6HiKmtbJuE2cpcH/pl7Vz6iTd9naGujOgl5uoGGGofFZWW3Iux+4DG9GJNZARxHo4qgm2RlViGjpB9V3Pz//9rwTga7/j+3jf+97HbD7LJCeGrERK1oovH+sXf/Wr65kYRhBziBDzccR6PDEmPnbXx/i022/gLz7txnxphMKM3P+aDujEECYY4FeT0iIj0RypGEe5RnakLn9IYn+Jmk80smEuRmqUEt/Xy5HynidWLHHFY3gYo2gd2h8jYPAh3lMYjM5ZYO5dwPkGYsepe+8m7m4RN9YQB323xLuC9Nc8HTgrwY4uZUKQqViZME2TW90VPQRBnIUfpTrCIwQcDscvvO6rDTO4JeBmD+CXIZd/K6FpeNvvvI+n3HgjN998s3kICm/68Xtq9yrnPQ8ctx5Cf/OLXmDehA/WVRvhM552Q81OKJJ1MLJfI/vjBTC04TMPppx/yQ+CqGQlfsngaCKQrAu3a3BOUI2IdlgdygVWWIqVpAy8iXztfDgHmKkXtPvLZVwxDBdhWOoy33yx5+SDxxEgxoLap0EhMRm5xoC+XCdR5eCEor9YMxTOWRepDPSFzFWwgqiiwzjgHVUXYmzWBP7G5z6H0DRM19YIk4bf+7c/zvQZ6/jQgBOa6YTQPLN2r6qrPNROVHsRxU+U8y+RyFiDw7p1r5IScrCSf88l3mLmpzjBSbI5HHv+5/ft7AuEPrYFWpfeuBJKPA6jBA7k1bbceSLQLWacOHHcGrzUPpdWQEVxr8tqOuIADKuqnAXYlUlUJn3Js+9N6dVIeXQ8MnpvredQrV5ISWm6LJYTvK/vKRjDarbhbBLQQFwaDMBKNWYlge2JPUoqxDYaQoWsOoWm0QQemJxXxoWPKx7D4zDsVvaZCBQBMR6Qg+NHj3L6wRNMQ4sJjxhHSAF1aqIxMrAgzVA4nAuZcGNzJZJVmUhIErxvcN6TEvQx4sUay9jbRxqalIyJHU9JPYrkDloIJkNpk770j/DedB+0hAsKmrIfnrGTsZGoIGme1znzOgCWBXfZs1pXGjZiNBCM0i3ZaIgmiL0RxbLQi1Zg9AmGCD6O44rH8DgNVcWJR3C5+tlgtONH76VbLvFirEiPYMymLLi+B3Evq+1K+FAn+FDa7Zz1jfDeW7dsN7ynakjqMDmTsakq6j72GAoNW3KasjSRKfqPoNbQduwdjL2E4onkjz0rnCieAwMpaa+nscpN0Pq7ZCISKYJGM7yq6BWD8IjGFY/hcRoasTRgnpHGDUg8cOw+Ur8kiVQ8obrUSREvaCoaCoJjFfiE4gH4Ool8lql3zuODCcHEPrvZK4g7QM5AeOv6tKJsbShcVty2jluCGzwENTBVq4ScHzW1LQYwp1FRfM4pnCtdWaOefYxCRldG7x61p0kRcdko4CDXW2hmTF5QwiB//l5MYWywngzjimF4LEdFto3zb6lFj5NE8B6vC47dfRdrwSNqJcsxx86lOayF3lonbeE3DKKyxi2wdnNWO0DKr2WJN0t7Ftm2coO7Id2fDURCcQI+d69WzcmQUq7tA9EOh1AmqricYbFeGCL2szrxkvtxVrhjZPg0XxxLMp4dRqz8roO3IVahCdahG03EuATfIt5bqCGtsS5zvcP+X4/tq16C/T74STquhBKP4ajYv5Rel1rFV1SV2dYWx4/eQxO8TQqR3JylTIQBwDvXF2XGwdXVvdYOuCFNWSoPnXMmNV8mZMZBfc4ilKpIl2sjcPawUD1ToQseKG4oDa2dqUr5NhUYdaUj+AoAOc4yFC9nn3qE0ao9GJH6jGEeA1gBsTfvgbN3s/exsp+SvciP/ezC5V5FeaG27opheFyG3XjOiTUzxTC67c3TzLfO4Pd+bQpilUe14/V4Oa1Tau/NKnuMQ01FZkZklsYvN/8wyYv7bH/Y+0PFMHSk3lwwCqndvHKKtLr6suKtOOez95O7YOuQhZDsTfhiWMTlYGN0Piu/DthC2axMci+y4iGMMzAlczp+mBG44hqca1wJJR6DsT9DLqEIcbmkaQKtg9PH7+fI+gQnsXZcMq17rN8DUHrbDMj+2Z9VKwCds0rIEtt7P0QzauFHTAlXQg7n6GNP31u1Zdu2hBAqMaoUS9Uu1aV3Re5b6VyATGqy3ptZFt8Ns2+s80DOrBSJlnHl4gqmkH8vnsdYz6LgJCUOqvTl4CBFUr+kCS3JW8+Mc63xl0q720t1XDEMj/LYzygMAFwiaqSRlvnuDkfv+The+9x/odz8+YZNOZZWzdV9q/sfl26vTi7r21kMg6XvBm6Aw0hTJpugw0RDUPGo2ArvnIU/3jWZxBTy8RnQ6XK2A+cRCRZWVI7BkCWJxSDsMQwr3s6eazbmQKidLIIgmo1KzupAaTKc30c0oDZFo5+f4/sYxhXjcK5xxTA8hmOMqmvODTYh0LYND5x4gBPH7sdJbtSShKjWI1NGZJ/xvvbe5Csly6NVNwTrVk3uOwkjEpFX5vM5Mca62vpcGFWJRWKZDh+s9LroQor5/pCrDnO3HoMZsocwkJbKmVMNg5QAX1bPQRj4DG7kKVSDMSRQzroulgY2ebnCgIyxy1JvZ29/ZZzfuGIYHuF4KApNSbPl6UufIm3riMs5R//8Y5w4dpSgia7vsb7agiugYKUtl3F2TUbNVJQwIv90Ia/6uTFtbVWvisaE772Ju2rRHcjsyewBWNiRXY2cRRCwEMT7rEDlRwbJvAWFkaCRvd+MVRY9Uc3OxCCAsmLrVGHUrckMRMFBSvNbze/xDJwG67MhRRmr73GhN4IXOngy1cIMV3G196gO2z3JxxXD8DCGy5PceVdDAOMXaE5NWvNaVSuWQp1xlpLl+mebJzj+5x9ldvo0ky5Z30VxSIxoinbju1LGrHXlK4SnFbXpUdzvc7fr0hjWVXk1hpVaIPiGJLnFX0zmrcSIn1C9jZiytFldcEtMYkYjacKrz3UfCpj8WwkTfNZ3sCmWTKgmaf69ALB2HloMiUhJSubzG300mcGQ06zO5T6cRERK41sDL5MmiEvwpkqFOiQZoJrE2gWa/dV8LGYwSlGWyzyICx3n8k0eTzPzaPlHl6RhuNTTQkUiHcmcA0yKjZTwzhE14oKn7yMxOUJoaXxAxRMauPvuj3L/h99H0y/wzTpRO9CET0uiRlJG62tAkdOYxWtIgukkNAHvQ/UUQpaER8xAaAYJyRNXMnJv70mUaAARouRukmJUaB9Cpl1bi7uStdCkSMz07JiQIqmeDZCFJQNTUrIbodUTUoRY04xVul6sOExhKK0uWgtqoZhdF89KKXblN5RwK4csaY5oIoQJaQnz+YK+TzTTCX6SDVqHxUFa0J2H7zFcCgHLQx3DhZ7ReRkGEfkYsIW1N+1V9bkicjXwS8BtwMeAL1PVU2Kz+qeAlwC7wNeo6h9d4HFd0qMoGsVoAiW2ytot7ZytWM5BaENWXu9Y9hElMkU4+vGPsX3mJEEi29tbtBsbxKj4JIgEa/zibJKVr7SyGp3DqeIlECSsNpARITQNWEBSQclkbzTKdV7FRQR1gxK00bVt9ffeZ1m3ZiXWLzhCHRU7LNkFhwtNzlBQmw7X9KSWqedWd1B/K57H4BEVqTYLm5xRx6Xs2/Qo0z4BnVNlubvLmZMnQIXlomc+X3Lw6qu5enotLgS6pGgsGEz2WET32duTb1wIj+EFqvpsVX1u/vt1wDtU9RnAO/LfAC8GnpEfrwL+xaN1sJfKSNntFDWaLypEFaII836J4ui6SL/sci/KiHiYTDzilXvu+giL7U1Ee0LjUY0ZDDTUP6nYDat7vp68KrpReFDCCPsz044L1pC1CmtNhXe58cxoRc9uuuRQxPuB1uxktZx7yFa4wRCspDBdNQLjysmVcY5l7ROtdlWsphSOVYHJkeHJWwsJ0chid5vjR+/j5NH7WGydopufYbFzktjt4kiVxq3iSWKelsu9KZ7s45EQnL4Y+Ln8+88BXzJ6/ufVxu8BR0TkxkfwOZfcSESi9jjXkFSYL3o6lBQ8zfoBzmzvIq5lMj1IM13HNxOch7ZxbD9wjAc+/jEmTknLOdM21BLjVDq7pyy26gZPAfKK6T0hhMpJ0By+OMS6R6WUJ3lZ7c3DCSEQMrVZShdsp4gvVARhMpmwtrY2wiqswY0PVl7tfCEtDcYo5WMo+g/kgiZJpUbBVvZHOkTAy2BsDO6wlEi1EaJ4STbpNXFwusb1V13NbPsU3fwU621PNzvN5slj9POZhT0hS9mpHe8Fyro8Ycf5YgwK/Fcxc/0vVfXNwA2qehRAVY+KyPV525uBu0fvvSc/d3S8QxF5FeZRcMsttzz8M7gII4RA3/c4dcSUaBDWNtZYdnOO3Xsfd99xJ4fWN9iYTjl81REOHT7IxqHD0C+564Pvp5vtsu6ELiaWy45UY+pcVu0yd0ETleGUR5mUqWbjrK7CMQopoNZUFK1GV8DMmMVPnJVkSU5NMtJxLO+jEpxGZdNl5Lhfk4GJfm+BVj02qa75KlVjb9bloTM8ZZeFMn52kdWwFxNvgbZpOHTwICLKbHebphWia4ldT0pqNGznAFPmNn6EPgpm7PIf52sY/rKq3pcn/2+IyAcfYtv9vtuzrnU2Lm8GePazn31ZfRcFEANbYULwSL/k5D338KH3vZf5mS2Oz2cst3e54YZrOHLkEE991qcRnPD+P3wPxJ4+pUJAQGPCSUClR7EV35UUo4SVtKSxED1Nkye6DobAZT4CiNEXRHBOaoigah6CElFJpH4QcHV55e36ntC0JhXnPUm15FvqUC0iKVAEafeSlIZU4qot0JLdKD/3vK0Uh+13zccNZ2ybcg1yFiNnGoqqdEoR7x0HDh7g5KnjzOZzfNvQNBNCaM2gih2YSso0dNuPnstCXabjQifYeRkGVb0v/3xARP4D8DzgmIjcmL2FG4EH8ub3ALeO3n4LcN8FHteFj5Lyeqx2v0JWgmVScNYuvp0G7vnYR/jjP3wPjYONqWMR4cz2A9yzfZT7iNz1wT9muex44Oif07oCqHn6PmXOQqrxs8GFWR1JxhoIQoom+laKk0R8LXJy3huOEPL7ckWk80ZzdgKiCZ8aSu/dGHO/zJRb26uasEtD9kyUPibwSivFAGUsIekoXZkNQEr0aeApOLEmvSWTI9gxA8ZoLFWk+bsb4yUrwxp61tRiAVg1Z1E0qzeVTIiTREpLFLju+mtZ9HNOnjzN9U+5gQOHriM060QkB9P2U1WRNBir/YzU450x229CP5wjuND3fEKMQUQ2RORg+R14IfCnwK8CX503+2rgP+bffxX4KrHxOcCZEnI8luOxNAp7R0q5UYxTfOtI2vPxuz9G183p45LklcmBNTaOHCSJEoJjvnmC3VNH8WnBcjlnvlzQRdMoLE56IQgNufysq1BqFfLKXFbPMmKO7+vEcA4d1SuMV1kpoKIY8OaDCck2TWsZi8xh6GNv5qlp8tUdkQoYHezoOMag4/i5lXLGspviQlzA11bTkuNd1M8ZkbgqBduMQ2gDSR2nTm9z9XU3srZ+GI2FTi1mcLxDi/o2ek7P5ckyzsdjuAH4D/kiBeAXVPU/i8h7gLeJyNcDHwe+NG//a1iq8iNYuvJrH/WjvohDgNRF2hBQTUxCYPPMKY4dvZ+mMXm2RUo0zjE5dIhl7CB2bD5wD3Gxk0MFpesjJGU6OUCKfa1gLC52ShkfCO6sz3diJCOX4/6kak1myKtqIT4BZPd7CEPAhWCYgBNcmQAymiTZC0gp0QZvx+JLbUIyZuSKoSjGZ6T8hHkPlRlQPK4S/owm3TgNulcQZainYPSZAGkkJW9GQNVVTUz7mIQQWS57YlKuuf5mDl99PeICsejiIiAe8S2kaA2CkxofIh+XCGd7MI/xuNixtVwK6rcisgV86GIfx3mOa4EHL/ZBnMe4XI4TLp9jvVyO8/9v7wxCqzqiMPz9qFGLpRrrImBpDHShC9EgtKKIiIUSpCsXSpeudNPSRUkQBJe6KFIQbEF3tdVSRQkUkarbWK2JRkI0gltdFOtW4biYk+Sam2gkzzvz4HxweXPn3sf9Xua98+ZO5syD2V0/NbM183lyKTMfxyvzI4pG0q12cG0XT2gf13bxhIW7xkItQRDUiMAQBEGNUgLDL7kF3oF2cW0XT2gf13bxhAW6FjH4GARBWZTSYwiCoCCyBwZJX0kalzQhqf/tz3ivLmckPZU0WqnrlHRV0kN/XOX1kvSTe9+V1Nuw6yeSrksak3Rf0rcl+kpaJummpBH3POr16yQNuec5SR1ev9T3J/x4dxOeFd9Fku5IGizc87Gke5KGJd3yuta1fTVNtumNlCH0COgBOoARYENGnx1ALzBaqTsO9Hu5Hzjm5T7gL9LMnC+AoYZdu4BeL38IPAA2lObr11vh5SXAkF//PLDP608BB718CDjl5X3AuYb/rt8DZ4FB3y/V8zHw8Yy6lrV9Yy9kjhe3FbhS2R8ABjI7dc8IDONAl5e7SHMuAH4G9s92XibvS8CXJfsCHwD/Ap+TJt8snvk+AK4AW7282M9TQ35rSWuL7AIG/YNUnKdfc7bA0LK2z30rMVeKdkm8ll4OvC29vHG8G7uZ9G1cnK93z4dJiXZXSb3EZ2b2chaXKU8//j+wuglP4ATwA9M/kLm6UE+YXgrhttISBtDCts8983FeKdqFUoS7pBXAn8B3Zvb8DYk/2Xwt/drsJkkrgYvA+je4ZPGUtAd4ama3Je2ch0vu9m/5UghVcvcY8qRovxtP5CtQqYT08gqSlpCCwq9mdsGri/U1s2fADdJ97kpJk19MVZcpTz/+EfBfA3rbgK+V1jf9nXQ7caJAT+D1pRBIwXZqKQR3WlDb5w4M/wCf+chvB2kQ53Jmp5kUlV4+iVLX4DQwZmY/luoraY33FJC0HNgNjAHXgb1zeE767wWumd8Yv0/MbMDM1ppZN+l9eM3MvinNExpaCqHJwac5BlH6SCPqj4DDmV1+Iy1B94IUZQ+Q7hv/Bh76Y6efK+Cke98DtjTsup3UHbwLDPvWV5ovsBG4456jwBGv7wFuktLz/wCWev0y35/w4z0Z3gc7mf6vRHGe7jTi2/3Jz00r2z5mPgZBUCP3rUQQBAUSgSEIghoRGIIgqBGBIQiCGhEYgiCoEYEhCIIaERiCIKgRgSEIghqvAOMHhtTfpChlAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def pose_estimation_one_folder(folder,saveFolder,mesh, isSaveObj=False):\n",
    "    \n",
    "    if not os.path.exists(saveFolder):\n",
    "        os.mkdir(saveFolder)\n",
    "    imgs, lms = load_imgs_and_centercrop(folder)# path to your image folder\n",
    "\n",
    "    img, lm = imgs.copy(), lms.copy()\n",
    "    img_size = [img.shape[2], img.shape[1]]\n",
    "    lm[...,1] = img_size[1] - lm[...,1]\n",
    "\n",
    "    MV,affine,scale,image_points,model_ids,mesh = optimizePose_Multiply_Img(mesh,lm,ids,steps=5)\n",
    "\n",
    "    for frameID,im in enumerate(img):\n",
    "\n",
    "        if CameraMode == 'ortho':\n",
    "            renderedImg, depth = render2(mesh,model=MV[frameID],scale=scale[frameID],resolution=im.shape[:2])\n",
    "        else:\n",
    "            renderedImg, depth = render(mesh,MV[frameID],scale[frameID][1,1],resolution=im.shape[:2])\n",
    "\n",
    "        cv2.imwrite(os.path.join(saveFolder,'%02d_render.png'%frameID),im)\n",
    "        \n",
    "        mask = np.sum(renderedImg,axis=2)>10\n",
    "        im[mask] = renderedImg[mask]*0.6 + im[mask]*0.4\n",
    "\n",
    "        im = visLM(mesh.vertices[model_ids[frameID]],image_points[frameID],im, affine[frameID],homogeneous=True)\n",
    "\n",
    "        plt.figure()\n",
    "        plt.imshow(im[...,::-1])\n",
    "        cv2.imwrite(os.path.join(saveFolder,'%02d_crop.png'%frameID),im)\n",
    "        np.savetxt(os.path.join(saveFolder,'%02d.txt'%frameID),affine[frameID])\n",
    "        \n",
    "    if isSaveObj:\n",
    "        mesh.export(os.path.join(saveFolder,'res.obj'))\n",
    "\n",
    "folder,saveFolder = 'sample','result'\n",
    "mesh.vertices = obj_base.reshape((-1,3))\n",
    "pose_estimation_one_folder(folder,saveFolder,mesh, isSaveObj=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.5"
  },
  "widgets": {
   "state": {},
   "version": "1.1.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
